Merge branch 'process_raw_data' into 'master'

Process raw data See merge request open-mmlab/mmdet.3d!54

Merge branch 'process_raw_data' into 'master'
Process raw data See merge request open-mmlab/mmdet.3d!54
7d8f56d8 · zhangwenwei · 66600f95 · b5209f39 · 7d8f56d8 · 7d8f56d8
Commit 7d8f56d8 authored Jun 17, 2020 by zhangwenwei
18 changed files
--- a/.gitignore
+++ b/.gitignore
@@ -125,3 +125,5 @@ exps/
 # demo
 *.jpg
 *.png
+/data/scannet/scans/
+/data/sunrgbd/OFFICIAL_SUNRGBD/
--- a/.isort.cfg
+++ b/.isort.cfg
@@ -3,6 +3,6 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmdet,mmdet3d
-known_third_party = cv2,matplotlib,mmcv,numba,numpy,nuscenes,pycocotools,pyquaternion,pytest,scipy,seaborn,shapely,six,skimage,terminaltables,torch,torchvision
+known_third_party = cv2,load_scannet_data,matplotlib,mmcv,numba,numpy,nuscenes,plyfile,pycocotools,pyquaternion,pytest,scannet_utils,scipy,seaborn,shapely,six,skimage,sunrgbd_utils,terminaltables,torch,torchvision
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
--- a/data/scannet/README.md
+++ b/data/scannet/README.md
+### Prepare ScanNet Data
+We follow the procedure in [votenet](https://github.com/facebookresearch/votenet/).
+1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Link or move the 'scans' folder to this level of directory.
+2. In this directory, extract point clouds and annotations by running `python batch_load_scannet_data.py`.
+3. Enter the project root directory, generate training data by running
+```bash
+python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet
+```
+The overall process could be achieved through the following script
+```bash
+python batch_load_scannet_data.py
+cd ../..
+python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet
+```
+The directory structure after pre-processing should be as below
+```
+scannet
+├── scannet_utils.py
+├── batch_load_scannet_data.py
+├── load_scannet_data.py
+├── scannet_utils.py
+├── README.md
+├── scans
+├── scannet_train_instance_data
+├── points
+├── instance_mask
+├── semantic_mask
+├── scannet_infos_train.pkl
+├── scannet_infos_val.pkl
+```
--- a/data/scannet/batch_load_scannet_data.py
+++ b/data/scannet/batch_load_scannet_data.py
+# Modified from
+# https://github.com/facebookresearch/votenet/blob/master/scannet/batch_load_scannet_data.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""Batch mode in loading Scannet scenes with vertices and ground truth labels
+for semantic and instance segmentations
+Usage example: python ./batch_load_scannet_data.py
+"""
+import argparse
+import datetime
+import os
+import os.path as osp
+import numpy as np
+from load_scannet_data import export
+SCANNET_DIR = 'scans'
+DONOTCARE_CLASS_IDS = np.array([])
+OBJ_CLASS_IDS = np.array(
+    [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
+def export_one_scan(scan_name, output_filename_prefix, max_num_point,
+                    label_map_file, scannet_dir):
+    mesh_file = osp.join(scannet_dir, scan_name, scan_name + '_vh_clean_2.ply')
+    agg_file = osp.join(scannet_dir, scan_name,
+                        scan_name + '.aggregation.json')
+    seg_file = osp.join(scannet_dir, scan_name,
+                        scan_name + '_vh_clean_2.0.010000.segs.json')
+    # includes axisAlignment info for the train set scans.
+    meta_file = osp.join(scannet_dir, scan_name, f'{scan_name}.txt')
+    mesh_vertices, semantic_labels, instance_labels, instance_bboxes, \
+        instance2semantic = export(mesh_file, agg_file, seg_file,
+                                   meta_file, label_map_file, None)
+    mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
+    mesh_vertices = mesh_vertices[mask, :]
+    semantic_labels = semantic_labels[mask]
+    instance_labels = instance_labels[mask]
+    num_instances = len(np.unique(instance_labels))
+    print(f'Num of instances: {num_instances}')
+    bbox_mask = np.in1d(instance_bboxes[:, -1], OBJ_CLASS_IDS)
+    instance_bboxes = instance_bboxes[bbox_mask, :]
+    print(f'Num of care instances: {instance_bboxes.shape[0]}')
+    N = mesh_vertices.shape[0]
+    if N > max_num_point:
+        choices = np.random.choice(N, max_num_point, replace=False)
+        mesh_vertices = mesh_vertices[choices, :]
+        semantic_labels = semantic_labels[choices]
+        instance_labels = instance_labels[choices]
+    np.save(f'{output_filename_prefix}_vert.npy', mesh_vertices)
+    np.save(f'{output_filename_prefix}_sem_label.npy', semantic_labels)
+    np.save(f'{output_filename_prefix}_ins_label.npy', instance_labels)
+    np.save(f'{output_filename_prefix}_bbox.npy', instance_bboxes)
+def batch_export(max_num_point, output_folder, train_scan_names_file,
+                 label_map_file, scannet_dir):
+    if not os.path.exists(output_folder):
+        print(f'Creating new data folder: {output_folder}')
+        os.mkdir(output_folder)
+    train_scan_names = [line.rstrip() for line in open(train_scan_names_file)]
+    for scan_name in train_scan_names:
+        print('-' * 20 + 'begin')
+        print(datetime.datetime.now())
+        print(scan_name)
+        output_filename_prefix = osp.join(output_folder, scan_name)
+        if osp.isfile(f'{output_filename_prefix}_vert.npy'):
+            print('File already exists. skipping.')
+            print('-' * 20 + 'done')
+            continue
+        try:
+            export_one_scan(scan_name, output_filename_prefix, max_num_point,
+                            label_map_file, scannet_dir)
+        except Exception:
+            print(f'Failed export scan: {scan_name}')
+        print('-' * 20 + 'done')
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--max_num_point',
+        default=50000,
+        help='The maximum number of the points.')
+    parser.add_argument(
+        '--output_folder',
+        default='./scannet_train_instance_data',
+        help='output folder of the result.')
+    parser.add_argument(
+        '--scannet_dir', default='scans', help='scannet data directory.')
+    parser.add_argument(
+        '--label_map_file',
+        default='meta_data/scannetv2-labels.combined.tsv',
+        help='The path of label map file.')
+    parser.add_argument(
+        '--train_scan_names_file',
+        default='meta_data/scannet_train.txt',
+        help='The path of the file that stores the scan names.')
+    args = parser.parse_args()
+    batch_export(args.max_num_point, args.output_folder,
+                 args.train_scan_names_file, args.label_map_file,
+                 args.scannet_dir)
+if __name__ == '__main__':
+    main()
--- a/data/scannet/load_scannet_data.py
+++ b/data/scannet/load_scannet_data.py
+# Modified from
+# https://github.com/facebookresearch/votenet/blob/master/scannet/load_scannet_data.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""Load Scannet scenes with vertices and ground truth labels
+for semantic and instance segmentations
+"""
+import argparse
+import inspect
+import json
+import os
+import numpy as np
+import scannet_utils
+currentdir = os.path.dirname(
+    os.path.abspath(inspect.getfile(inspect.currentframe())))
+def read_aggregation(filename):
+    assert os.path.isfile(filename)
+    object_id_to_segs = {}
+    label_to_segs = {}
+    with open(filename) as f:
+        data = json.load(f)
+        num_objects = len(data['segGroups'])
+        for i in range(num_objects):
+            object_id = data['segGroups'][i][
+                'objectId'] + 1  # instance ids should be 1-indexed
+            label = data['segGroups'][i]['label']
+            segs = data['segGroups'][i]['segments']
+            object_id_to_segs[object_id] = segs
+            if label in label_to_segs:
+                label_to_segs[label].extend(segs)
+            else:
+                label_to_segs[label] = segs
+    return object_id_to_segs, label_to_segs
+def read_segmentation(filename):
+    assert os.path.isfile(filename)
+    seg_to_verts = {}
+    with open(filename) as f:
+        data = json.load(f)
+        num_verts = len(data['segIndices'])
+        for i in range(num_verts):
+            seg_id = data['segIndices'][i]
+            if seg_id in seg_to_verts:
+                seg_to_verts[seg_id].append(i)
+            else:
+                seg_to_verts[seg_id] = [i]
+    return seg_to_verts, num_verts
+def export(mesh_file,
+           agg_file,
+           seg_file,
+           meta_file,
+           label_map_file,
+           output_file=None):
+    """Export original files to vert, ins_label, sem_label and bbox file.
+    Args:
+        mesh_file(str): Path of the mesh_file.
+        agg_file(str): Path of the agg_file.
+        seg_file(str): Path of the seg_file.
+        meta_file(str): Path of the meta_file.
+        label_map_file(str): Path of the label_map_file.
+        output_file(str): Path of the output folder.
+            Default: None.
+    It returns a tuple, which containts the the following things:
+        ndarray: Vertices of points data.
+        ndarray: Indexes of label.
+        ndarray: Indexes of instance.
+        ndarray: Instance bboxes.
+        dict: Map from object_id to label_id.
+    """
+    label_map = scannet_utils.read_label_mapping(
+        label_map_file, label_from='raw_category', label_to='nyu40id')
+    mesh_vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file)
+    # Load scene axis alignment matrix
+    lines = open(meta_file).readlines()
+    for line in lines:
+        if 'axisAlignment' in line:
+            axis_align_matrix = [
+                float(x)
+                for x in line.rstrip().strip('axisAlignment = ').split(' ')
+            ]
+            break
+    axis_align_matrix = np.array(axis_align_matrix).reshape((4, 4))
+    pts = np.ones((mesh_vertices.shape[0], 4))
+    pts[:, 0:3] = mesh_vertices[:, 0:3]
+    pts = np.dot(pts, axis_align_matrix.transpose())  # Nx4
+    mesh_vertices[:, 0:3] = pts[:, 0:3]
+    # Load semantic and instance labels
+    object_id_to_segs, label_to_segs = read_aggregation(agg_file)
+    seg_to_verts, num_verts = read_segmentation(seg_file)
+    label_ids = np.zeros(shape=(num_verts), dtype=np.uint32)
+    object_id_to_label_id = {}
+    for label, segs in label_to_segs.items():
+        label_id = label_map[label]
+        for seg in segs:
+            verts = seg_to_verts[seg]
+            label_ids[verts] = label_id
+    instance_ids = np.zeros(
+        shape=(num_verts), dtype=np.uint32)  # 0: unannotated
+    num_instances = len(np.unique(list(object_id_to_segs.keys())))
+    for object_id, segs in object_id_to_segs.items():
+        for seg in segs:
+            verts = seg_to_verts[seg]
+            instance_ids[verts] = object_id
+            if object_id not in object_id_to_label_id:
+                object_id_to_label_id[object_id] = label_ids[verts][0]
+    instance_bboxes = np.zeros((num_instances, 7))
+    for obj_id in object_id_to_segs:
+        label_id = object_id_to_label_id[obj_id]
+        obj_pc = mesh_vertices[instance_ids == obj_id, 0:3]
+        if len(obj_pc) == 0:
+            continue
+        xmin = np.min(obj_pc[:, 0])
+        ymin = np.min(obj_pc[:, 1])
+        zmin = np.min(obj_pc[:, 2])
+        xmax = np.max(obj_pc[:, 0])
+        ymax = np.max(obj_pc[:, 1])
+        zmax = np.max(obj_pc[:, 2])
+        bbox = np.array([(xmin + xmax) / 2, (ymin + ymax) / 2,
+                         (zmin + zmax) / 2, xmax - xmin, ymax - ymin,
+                         zmax - zmin, label_id])
+        # NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES
+        instance_bboxes[obj_id - 1, :] = bbox
+    if output_file is not None:
+        np.save(output_file + '_vert.npy', mesh_vertices)
+        np.save(output_file + '_sem_label.npy', label_ids)
+        np.save(output_file + '_ins_label.npy', instance_ids)
+        np.save(output_file + '_bbox.npy', instance_bboxes)
+    return mesh_vertices, label_ids, instance_ids,\
+        instance_bboxes, object_id_to_label_id
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--scan_path',
+        required=True,
+        help='path to scannet scene (e.g., data/ScanNet/v2/scene0000_00')
+    parser.add_argument('--output_file', required=True, help='output file')
+    parser.add_argument(
+        '--label_map_file',
+        required=True,
+        help='path to scannetv2-labels.combined.tsv')
+    opt = parser.parse_args()
+    scan_name = os.path.split(opt.scan_path)[-1]
+    mesh_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.ply')
+    agg_file = os.path.join(opt.scan_path, scan_name + '.aggregation.json')
+    seg_file = os.path.join(opt.scan_path,
+                            scan_name + '_vh_clean_2.0.010000.segs.json')
+    meta_file = os.path.join(
+        opt.scan_path, scan_name +
+        '.txt')  # includes axisAlignment info for the train set scans.
+    export(mesh_file, agg_file, seg_file, meta_file, opt.label_map_file,
+           opt.output_file)
+if __name__ == '__main__':
+    main()
--- a/data/scannet/meta_data/scannet_means.npz
+++ b/data/scannet/meta_data/scannet_means.npz
--- a/data/scannet/meta_data/scannet_train.txt
+++ b/data/scannet/meta_data/scannet_train.txt
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--- a/data/scannet/meta_data/scannetv2-labels.combined.tsv
+++ b/data/scannet/meta_data/scannetv2-labels.combined.tsv
+id	raw_category	category	count	nyu40id	eigen13id	nyuClass	nyu40class	eigen13class	ModelNet40	ModelNet10	ShapeNetCore55	synsetoffset	wnsynsetid	wnsynsetkey	mpcat40	mpcat40index
+1	wall	wall	8277	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+2	chair	chair	4646	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+22	books	book	1678	23	2	book	books	Books					n02870526	book.n.11	objects	39
+3	floor	floor	1553	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+5	door	door	1483	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+1163	object	object	1313	40	7		otherprop	Objects							objects	39
+16	window	window	1209	9	13	window	window	Window					n04587648	window.n.01	window	9
+4	table	table	1170	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+56	trash can	trash can	1090	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+13	pillow	pillow	937	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+15	picture	picture	862	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+41	ceiling	ceiling	806	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+26	box	box	775	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+161	doorframe	doorframe	768	8	12	door	door	Wall	door					doorframe.n.01	door	4
+19	monitor	monitor	765	40	7	monitor	otherprop	Objects	monitor	monitor	tv or monitor	3211117	n03782190	monitor.n.04	objects	39
+7	cabinet	cabinet	731	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+9	desk	desk	680	14	10	desk	desk	Table	desk	desk	table	4379243	n03179701	desk.n.01	table	5
+8	shelf	shelf	641	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+10	office chair	office chair	595	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04373704	swivel_chair.n.01	chair	3
+31	towel	towel	570	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+6	couch	couch	502	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+14	sink	sink	488	34	7	sink	sink	Objects	sink				n04223580	sink.n.01	sink	15
+48	backpack	backpack	479	40	7	backpack	otherprop	Objects					n02769748	backpack.n.01	objects	39
+28	lamp	lamp	419	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+11	bed	bed	370	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+18	bookshelf	bookshelf	360	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+71	mirror	mirror	349	19	7	mirror	mirror	Objects					n03773035	mirror.n.01	mirror	21
+21	curtain	curtain	347	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+40	plant	plant	331	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+52	whiteboard	whiteboard	327	30	7	whiteboard	whiteboard	Objects					n03211616	display_panel.n.01	board_panel	35
+96	radiator	radiator	322	39	6	radiator	otherfurniture	Furniture					n04041069	radiator.n.02	misc	40
+22	book	book	318	23	2	book	books	Books					n02870526	book.n.11	objects	39
+29	kitchen cabinet	kitchen cabinet	310	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+49	toilet paper	toilet paper	291	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+29	kitchen cabinets	kitchen cabinet	289	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+23	armchair	armchair	281	5	4	chair	chair	Chair	chair	chair	chair	3001627	n02738535	armchair.n.01	chair	3
+63	shoes	shoe	272	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+24	coffee table	coffee table	258	7	10	coffee table	table	Table	table	table	table	4379243	n03063968	coffee_table.n.01	table	5
+17	toilet	toilet	256	33	7	toilet	toilet	Objects	toilet	toilet			n04446276	toilet.n.01	toilet	18
+47	bag	bag	252	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+32	clothes	clothes	248	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+46	keyboard	keyboard	246	40	7	keyboard	otherprop	Objects	keyboard		computer keyboard	3085013	n03085013	computer_keyboard.n.01	objects	39
+65	bottle	bottle	226	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+97	recycling bin	recycling bin	225	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+34	nightstand	nightstand	224	32	6	night stand	night stand	Furniture	night_stand	night_stand			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+38	stool	stool	221	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+33	tv	tv	219	25	11	television	television	TV			tv or monitor	3211117	n03211117	display.n.06	tv_monitor	22
+75	file cabinet	file cabinet	217	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+36	dresser	dresser	213	17	6	dresser	dresser	Furniture	dresser	dresser			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+64	computer tower	computer tower	203	40	7	computer	otherprop	Objects					n03082979	computer.n.01	objects	39
+32	clothing	clothes	165	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+101	telephone	telephone	164	40	7	telephone	otherprop	Objects			telephone	4401088	n04401088	telephone.n.01	objects	39
+130	cup	cup	157	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+27	refrigerator	refrigerator	154	24	6	refridgerator	refridgerator	Furniture					n04070727	refrigerator.n.01	appliances	37
+44	end table	end table	147	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+131	jacket	jacket	146	40	7	jacket	otherprop	Objects					n03589791	jacket.n.01	clothes	38
+55	shower curtain	shower curtain	144	28	7	shower curtain	shower curtain	Objects	curtain				n04209239	shower_curtain.n.01	curtain	12
+42	bathtub	bathtub	144	36	7	bathtub	bathtub	Objects	bathtub	bathtub	tub	2808440	n02808440	bathtub.n.01	bathtub	25
+59	microwave	microwave	141	40	7	microwave	otherprop	Objects			microwave	3761084	n03761084	microwave.n.02	appliances	37
+159	kitchen counter	kitchen counter	140	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+74	sofa chair	sofa chair	129	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+82	paper towel dispenser	paper towel dispenser	129	40	7	paper towel dispenser	otherprop	Objects							objects	39
+1164	bathroom vanity	bathroom vanity	126	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	table	5
+93	suitcase	suitcase	118	40	7	luggage	otherprop	Objects					n02773838	bag.n.06	objects	39
+77	laptop	laptop	111	40	7	laptop	otherprop	Objects	laptop		laptop	3642806	n03642806	laptop.n.01	objects	39
+67	ottoman	ottoman	111	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+128	shower walls	shower wall	109	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+50	printer	printer	106	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+35	counter	counter	104	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+69	board	board	100	38	7	board	otherstructure	Objects							board_panel	35
+100	soap dispenser	soap dispenser	99	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+62	stove	stove	95	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+105	light	light	93	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+1165	closet wall	closet wall	90	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+165	mini fridge	mini fridge	87	24	6	refridgerator	refridgerator	Furniture					n03273913	electric_refrigerator.n.01	appliances	37
+7	cabinets	cabinet	79	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+5	doors	door	76	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+76	fan	fan	75	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+230	tissue box	tissue box	73	40	7	tissue box	otherprop	Objects					n02883344	box.n.01	objects	39
+54	blanket	blanket	72	40	7	blanket	otherprop	Objects					n02849154	blanket.n.01	objects	39
+125	bathroom stall	bathroom stall	71	38	7		otherstructure	Objects					n02873839	booth.n.02	misc	40
+72	copier	copier	70	40	7		otherprop	Objects					n03257586	duplicator.n.01	appliances	37
+68	bench	bench	66	39	6	bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+145	bar	bar	66	38	7	bar	otherstructure	Objects					n02788689	bar.n.03	misc	40
+157	soap dish	soap dish	65	40	7	soap dish	otherprop	Objects					n04254009	soap_dish.n.01	objects	39
+1166	laundry hamper	laundry hamper	65	40	7	laundry basket	otherprop	Objects							objects	39
+132	storage bin	storage bin	63	40	7	storage bin	otherprop	Objects							objects	39
+1167	bathroom stall door	bathroom stall door	62	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+232	light switch	light switch	61	38	7	light switch	otherstructure	Objects					n04372370	switch.n.01	misc	40
+134	coffee maker	coffee maker	61	40	7		otherprop	Objects					n03063338	coffee_maker.n.01	appliances	37
+51	tv stand	tv stand	61	39	6	tv stand	otherfurniture	Furniture	tv_stand				n03290653	entertainment_center.n.01	furniture	36
+250	decoration	decoration	60	40	7		otherprop	Objects					n03169390	decoration.n.01	misc	40
+1168	ceiling light	ceiling light	59	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+342	range hood	range hood	59	38	7	range hood	otherstructure	Objects	range_hood				n04053677	range_hood.n.01	misc	40
+89	blackboard	blackboard	58	38	7	blackboard	otherstructure	Objects					n02846511	blackboard.n.01	board_panel	35
+103	clock	clock	58	40	7	clock	otherprop	Objects			clock	3046257	n03046257	clock.n.01	objects	39
+99	wardrobe closet	wardrobe	54	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+95	rail	rail	53	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+154	bulletin board	bulletin board	53	38	7	board	otherstructure	Objects					n03211616	display_panel.n.01	board_panel	35
+140	mat	mat	52	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1169	trash bin	trash bin	52	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+193	ledge	ledge	51	38	7		otherstructure	Objects					n09337253	ledge.n.01	misc	40
+116	seat	seat	49	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+202	mouse	mouse	49	40	7	mouse	otherprop	Objects					n03793489	mouse.n.04	objects	39
+73	basket	basket	48	40	7	basket	otherprop	Objects			basket	2801938	n02801938	basket.n.01	objects	39
+78	shower	shower	48	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+1170	dumbbell	dumbbell	48	40	7		otherprop	Objects					n03255030	dumbbell.n.01	objects	39
+79	paper	paper	46	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+80	person	person	46	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+141	windowsill	windowsill	45	38	7		otherstructure	Objects					n04590263	windowsill.n.01	window	9
+57	closet	closet	45	39	6	wardrobe	otherfurniture	Furniture	wardrobe						misc	40
+102	bucket	bucket	45	40	7	bucket	otherprop	Objects					n02909870	bucket.n.01	misc	40
+261	sign	sign	44	40	7	sign	otherprop	Objects					n04217882	signboard.n.01	objects	39
+118	speaker	speaker	43	40	7	speaker	otherprop	Objects			speaker	3691459	n03691459	loudspeaker.n.01	objects	39
+136	dishwasher	dishwasher	43	38	7	dishwasher	otherstructure	Objects			dishwasher	3207941	n03207941	dishwasher.n.01	appliances	37
+98	container	container	43	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1171	stair rail	stair rail	42	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+170	shower curtain rod	shower curtain rod	42	40	7		otherprop	Objects							curtain	12
+1172	tube	tube	41	40	7		otherprop	Objects							misc	40
+1173	bathroom cabinet	bathroom cabinet	39	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+79	papers	paper	39	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+221	storage container	storage container	39	40	7	container	otherprop	Objects							objects	39
+570	paper bag	paper bag	39	37	7	bag	bag	Objects					n04122825	sack.n.01	objects	39
+138	paper towel roll	paper towel roll	39	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+168	ball	ball	39	40	7	ball	otherprop	Objects							objects	39
+276	closet doors	closet door	38	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+106	laundry basket	laundry basket	37	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+214	cart	cart	37	40	7	cart	otherprop	Objects					n03484083	handcart.n.01	shelving	31
+276	closet door	closet door	35	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+323	dish rack	dish rack	35	40	7	dish rack	otherprop	Objects					n03207630	dish_rack.n.01	objects	39
+58	stairs	stairs	35	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+86	blinds	blinds	35	13	13	blinds	blinds	Window					n02851099	blind.n.03	blinds	32
+2	stack of chairs	chair	35	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+399	purse	purse	34	40	7	purse	otherprop	Objects					n02774152	bag.n.04	objects	39
+121	bicycle	bicycle	33	40	7	bicycle	otherprop	Objects			bicycle	2834778	n02834778	bicycle.n.01	objects	39
+185	tray	tray	32	40	7	tray	otherprop	Objects					n04476259	tray.n.01	objects	39
+300	plunger	plunger	30	40	7		otherprop	Objects					n03970156	plunger.n.03	objects	39
+180	paper cutter	paper cutter	30	40	7	paper cutter	otherprop	Objects					n03886940	paper_cutter.n.01	objects	39
+163	toilet paper dispenser	toilet paper dispenser	29	40	7		otherprop	Objects							objects	39
+26	boxes	box	29	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+66	bin	bin	28	40	7	bin	otherprop	Objects					n02839910	bin.n.01	objects	39
+208	toilet seat cover dispenser	toilet seat cover dispenser	28	40	7		otherprop	Objects							objects	39
+112	guitar	guitar	28	40	7	guitar	otherprop	Objects	guitar		guitar	3467517	n03467517	guitar.n.01	objects	39
+540	mailboxes	mailbox	28	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+395	handicap bar	handicap bar	27	38	7	bar	otherstructure	Objects							misc	40
+166	fire extinguisher	fire extinguisher	27	40	7	fire extinguisher	otherprop	Objects					n03345837	fire_extinguisher.n.01	misc	40
+122	ladder	ladder	27	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	stairs	16
+120	column	column	26	38	7	column	otherstructure	Objects					n03074380	column.n.06	column	24
+107	pipe	pipe	25	40	7	pipe	otherprop	Objects					n03944672	pipe.n.02	misc	40
+283	vacuum cleaner	vacuum cleaner	25	40	7		otherprop	Objects					n04517823	vacuum.n.04	objects	39
+88	plate	plate	24	40	7	plate	otherprop	Objects					n03959485	plate.n.04	objects	39
+90	piano	piano	24	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+177	water cooler	water cooler	24	39	6	water cooler	otherfurniture	Furniture					n04559166	water_cooler.n.01	misc	40
+1174	cd case	cd case	24	40	7		otherprop	Objects							objects	39
+562	bowl	bowl	24	40	7	bowl	otherprop	Objects	bowl		bowl	2880940	n02880940	bowl.n.03	objects	39
+1175	closet rod	closet rod	24	40	7		otherprop	Objects					n04100174	rod.n.01	misc	40
+1156	bathroom counter	bathroom counter	24	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+84	oven	oven	23	38	7	oven	otherstructure	Objects					n03862676	oven.n.01	appliances	37
+104	stand	stand	23	39	6	stand	otherfurniture	Furniture	table	table	table	4379243	n04301000	stand.n.04	table	5
+229	scale	scale	23	40	7	scale	otherprop	Objects					n04141975	scale.n.07	objects	39
+70	washing machine	washing machine	23	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+325	broom	broom	22	40	7	broom	otherprop	Objects					n02906734	broom.n.01	objects	39
+169	hat	hat	22	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+128	shower wall	shower wall	22	1	12	wall	wall	Wall					n04208936	shower.n.01	wall	1
+331	guitar case	guitar case	21	40	7	guitar case	otherprop	Objects							objects	39
+87	rack	rack	21	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+488	water pitcher	water pitcher	21	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+776	laundry detergent	laundry detergent	21	40	7		otherprop	Objects							objects	39
+370	hair dryer	hair dryer	21	40	7	hair dryer	otherprop	Objects					n03483316	hand_blower.n.01	objects	39
+191	pillar	pillar	21	38	7	column	otherstructure	Objects					n03073977	column.n.07	column	24
+748	divider	divider	20	40	7		otherprop	Objects							wall	1
+242	power outlet	power outlet	19	40	7		otherprop	Objects							misc	40
+45	dining table	dining table	19	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+417	shower floor	shower floor	19	2	5	floor	floor	Floor					n04208936	shower.n.01	floor	2
+70	washing machines	washing machine	19	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+188	shower door	shower door	19	8	12	door	door	Wall	door				n04208936	shower.n.01	door	4
+1176	coffee kettle	coffee kettle	18	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1177	wardrobe cabinet	wardrobe 	18	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1178	structure	structure	18	38	7		otherstructure	Objects							misc	40
+18	bookshelves	bookshelf	17	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+110	clothes dryer	clothes dryer	17	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+148	toaster	toaster	17	40	7	toaster	otherprop	Objects					n04442312	toaster.n.02	appliances	37
+63	shoe	shoe	17	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+155	ironing board	ironing board	16	39	6	ironing board	otherfurniture	Furniture					n03586090	ironing_board.n.01	objects	39
+572	alarm clock	alarm clock	16	40	7	alarm clock	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+1179	shower head	shower head	15	38	7		otherstructure	Objects							shower	23
+28	lamp base	lamp	15	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+392	water bottle	water bottle	15	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n04557648	water_bottle.n.01	objects	39
+1180	keyboard piano	keyboard piano	15	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+609	projector screen	projector screen	15	38	7	projector screen	otherstructure	Objects							misc	40
+1181	case of water bottles	case of water bottles	15	40	7		otherprop	Objects							objects	39
+195	toaster oven	toaster oven	14	40	7	toaster oven	otherprop	Objects					n04442441	toaster_oven.n.01	appliances	37
+581	music stand	music stand	14	39	6	music stand	otherfurniture	Furniture					n03801760	music_stand.n.01	furniture	36
+58	staircase	stairs	14	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+1182	coat rack	coat rack	14	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	3
+1183	storage organizer	storage organizer	14	40	7		otherprop	Objects							shelving	3
+139	machine	machine	14	40	7	machine	otherprop	Objects					n03699975	machine.n.01	appliances	37
+1184	folded chair	folded chair	14	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1185	fire alarm	fire alarm	14	40	7		otherprop	Objects					n03343737	fire_alarm.n.02	misc	40
+156	fireplace	fireplace	13	38	7	fireplace	otherstructure	Objects					n03346455	fireplace.n.01	fireplace	27
+408	vent	vent	13	40	7		otherprop	Objects					n04526241	vent.n.01	misc	40
+213	furniture	furniture	13	39	6	furniture	otherfurniture	Furniture					n03405725	furniture.n.01	furniture	36
+1186	power strip	power strip	13	40	7		otherprop	Objects							objects	39
+1187	calendar	calendar	13	40	7		otherprop	Objects							objects	39
+1188	poster	poster	13	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+115	toilet paper holder	toilet paper holder	13	40	7	toilet paper holder	otherprop	Objects							objects	39
+1189	potted plant	potted plant	12	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+304	stuffed animal	stuffed animal	12	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+1190	luggage	luggage	12	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+21	curtains	curtain	12	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+312	headphones	headphones	12	40	7		otherprop	Objects					n03261776	earphone.n.01	objects	39
+233	crate	crate	12	39	6	crate	otherfurniture	Furniture					n03127925	crate.n.01	objects	39
+286	candle	candle	12	40	7	candle	otherprop	Objects	lamp				n02948072	candle.n.01	objects	39
+264	projector	projector	12	40	7	projector	otherprop	Objects					n04009552	projector.n.02	objects	39
+110	clothes dryers	clothes dryer	12	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+1191	mattress	mattress	12	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+356	dustpan	dustpan	12	40	7		otherprop	Objects					n03259009	dustpan.n.02	objects	39
+25	drawer	drawer	11	39	6	drawer	otherfurniture	Furniture					n03233905	drawer.n.01	furniture	36
+750	rod	rod	11	40	7		otherprop	Objects			pistol	3948459	n03427202	gat.n.01	misc	40
+269	globe	globe	11	40	7	globe	otherprop	Objects							objects	39
+307	footrest	footrest	11	39	6	foot rest	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+410	piano bench	piano bench	11	39	6	piano bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+730	breakfast bar	breakfast bar	11	38	7	bar	otherstructure	Objects							counter	26
+216	step stool	step stool	11	40	7	step stool	otherprop	Objects	stool				n04315713	step_stool.n.01	stool	19
+1192	hand rail	hand rail	11	38	7	railing	otherstructure	Objects							railing	30
+119	vending machine	vending machine	11	40	7	machine	otherprop	Objects					n04525305	vending_machine.n.01	appliances	37
+682	ceiling fan	ceiling fan	11	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+434	swiffer	swiffer	11	40	7		otherprop	Objects							objects	39
+126	foosball table	foosball table	11	39	6	foosball table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+919	jar	jar	11	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+85	footstool	footstool	11	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+1193	folded table	folded table	10	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+108	round table	round table	10	7	10	table	table	Table	table	table	table	4379243	n04114554	round_table.n.02	table	5
+135	hamper	hamper	10	40	7	basket	otherprop	Objects			basket	2801938	n03482405	hamper.n.02	objects	39
+1194	poster tube	poster tube	10	40	7		otherprop	Objects							objects	39
+432	case	case	10	40	7	case	otherprop	Objects							objects	39
+53	carpet	carpet	10	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+1195	thermostat	thermostat	10	40	7		otherprop	Objects					n04422875	thermostat.n.01	misc	40
+111	coat	coat	10	40	7	jacket	otherprop	Objects					n03057021	coat.n.01	clothes	38
+305	water fountain	water fountain	10	38	7	water fountain	otherstructure	Objects					n03241335	drinking_fountain.n.01	misc	40
+1125	smoke detector	smoke detector	10	40	7		otherprop	Objects							misc	40
+13	pillows	pillow	9	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+1196	flip flops	flip flops	9	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1197	cloth	cloth	9	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1198	banner	banner	9	40	7		otherprop	Objects					n02788021	banner.n.01	misc	40
+1199	clothes hanger	clothes hanger	9	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	objects	39
+1200	whiteboard eraser	whiteboard eraser	9	40	7		otherprop	Objects							objects	39
+378	iron	iron	9	40	7		otherprop	Objects					n03584829	iron.n.04	objects	39
+591	instrument case	instrument case	9	40	7	case	otherprop	Objects							objects	39
+49	toilet paper rolls	toilet paper	9	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+92	soap	soap	9	40	7	soap	otherprop	Objects					n04253437	soap.n.01	objects	39
+1098	block	block	9	40	7		otherprop	Objects							misc	40
+291	wall hanging	wall hanging	8	40	7		otherprop	Objects					n03491178	hanging.n.01	picture	6
+1063	kitchen island	kitchen island	8	38	7	kitchen island	otherstructure	Objects					n03620600	kitchen_island.n.01	counter	26
+107	pipes	pipe	8	38	7		otherstructure	Objects							misc	40
+1135	toothbrush	toothbrush	8	40	7	toothbrush	otherprop	Objects					n04453156	toothbrush.n.01	objects	39
+189	shirt	shirt	8	40	7		otherprop	Objects					n04197391	shirt.n.01	clothes	38
+245	cutting board	cutting board	8	40	7	cutting board	otherprop	Objects					n03025513	chopping_board.n.01	objects	39
+194	vase	vase	8	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1201	shower control valve	shower control valve	8	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+386	exercise machine	exercise machine	8	40	7	machine	otherprop	Objects							gym_equipment	33
+1202	compost bin	compost bin	8	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+857	shorts	shorts	8	40	7	shorts	otherprop	Objects							clothes	38
+452	tire	tire	8	40	7		otherprop	Objects					n04440749	tire.n.01	objects	39
+1203	teddy bear	teddy bear	7	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+346	bathrobe	bathrobe	7	40	7		otherprop	Objects					n02807616	bathrobe.n.01	clothes	38
+152	handrail	handrail	7	38	7	railing	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+83	faucet	faucet	7	40	7	faucet	otherprop	Objects			faucet	3325088	n03325088	faucet.n.01	misc	40
+1204	pantry wall	pantry wall	7	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+726	thermos	thermos	7	40	7	flask	otherprop	Objects	bottle		bottle	2876657	n04422727	thermos.n.01	objects	39
+61	rug	rug	7	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+39	couch cushions	cushion	7	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1117	tripod	tripod	7	39	6	stand	otherfurniture	Furniture					n04485082	tripod.n.01	objects	39
+540	mailbox	mailbox	7	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+1205	tupperware	tupperware	7	40	7		otherprop	Objects							objects	39
+415	shoe rack	shoe rack	7	40	7	shoe rack	otherprop	Objects							shelving	31
+31	towels	towel	6	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+1206	beer bottles	beer bottle	6	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+153	treadmill	treadmill	6	39	6	treadmill	otherfurniture	Furniture					n04477387	treadmill.n.01	gym_equipment	33
+1207	salt	salt	6	40	7		otherprop	Objects							objects	39
+129	chest	chest	6	39	6	chest	otherfurniture	Furniture	dresser	dresser					chest_of_drawers	13
+220	dispenser	dispenser	6	40	7		otherprop	Objects					n03210683	dispenser.n.01	objects	39
+1208	mirror doors	mirror door	6	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+231	remote	remote	6	40	7		otherprop	Objects			remote_control	4074963	n04074963	remote_control.n.01	objects	39
+1209	folded ladder	folded ladder	6	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	misc	40
+39	cushion	cushion	6	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1210	carton	carton	6	40	7		otherprop	Objects							objects	39
+117	step	step	6	38	7		otherstructure	Objects					n04314914	step.n.04	misc	40
+822	drying rack	drying rack	6	39	6	drying rack	otherfurniture	Furniture							shelving	31
+238	slippers	slipper	6	40	7	shoe	otherprop	Objects					n04241394	slipper.n.01	clothes	38
+143	pool table	pool table	6	39	6	pool table	otherfurniture	Furniture	table	table	table	4379243	n03982430	pool_table.n.01	table	5
+1211	soda stream	soda stream	6	40	7		otherprop	Objects							objects	39
+228	toilet brush	toilet brush	6	40	7	toilet brush	otherprop	Objects							objects	39
+494	loft bed	loft bed	6	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+226	cooking pot	cooking pot	6	40	7	pot	otherprop	Objects							objects	39
+91	heater	heater	6	39	6	heater	otherfurniture	Furniture					n03508101	heater.n.01	misc	40
+1072	messenger bag	messenger bag	6	37	7	bag	bag	Objects							objects	39
+435	stapler	stapler	6	40	7	stapler	otherprop	Objects					n04303497	stapler.n.01	objects	39
+1165	closet walls	closet wall	5	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+345	scanner	scanner	5	40	7		otherprop	Objects							appliances	37
+893	elliptical machine	elliptical machine	5	40	7	machine	otherprop	Objects							gym_equipment	33
+621	kettle	kettle	5	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1212	metronome	metronome	5	40	7		otherprop	Objects					n03757604	metronome.n.01	objects	39
+297	dumbell	dumbell	5	40	7		otherprop	Objects							objects	39
+1213	music book	music book	5	23	2	book	books	Books					n02870526	book.n.11	objects	39
+1214	rice cooker	rice cooker	5	40	7		otherprop	Objects							objects	39
+1215	dart board	dart board	5	38	7	board	otherstructure	Objects					n03162940	dartboard.n.01	objects	39
+529	sewing machine	sewing machine	5	40	7	sewing machine	otherprop	Objects					n04179913	sewing_machine.n.01	objects	39
+1216	grab bar	grab bar	5	38	7	railing	otherstructure	Objects							railing	30
+1217	flowerpot	flowerpot	5	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1218	painting	painting	5	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1219	railing	railing	5	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+1220	stair	stair	5	38	7	stairs	otherstructure	Objects	stairs				n04314914	step.n.04	stairs	16
+525	toolbox	toolbox	5	39	6	chest	otherfurniture	Furniture					n04452615	toolbox.n.01	objects	39
+204	nerf gun	nerf gun	5	40	7		otherprop	Objects							objects	39
+693	binders	binder	5	40	7	binder	otherprop	Objects							objects	39
+179	desk lamp	desk lamp	5	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1221	quadcopter	quadcopter	5	40	7		otherprop	Objects							objects	39
+1222	pitcher	pitcher	5	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+1223	hanging	hanging	5	40	7		otherprop	Objects							misc	40
+1224	mail	mail	5	40	7		otherprop	Objects							misc	40
+1225	closet ceiling	closet ceiling	5	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+1226	hoverboard	hoverboard	5	40	7		otherprop	Objects							objects	39
+1227	beanbag chair	beanbag chair	5	39	6	bean bag	otherfurniture	Furniture					n02816656	beanbag.n.01	chair	3
+571	water heater	water heater	5	40	7	water heater	otherprop	Objects					n04560113	water_heater.n.01	misc	40
+1228	spray bottle	spray bottle	5	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+556	rope	rope	5	40	7	rope	otherprop	Objects					n04108268	rope.n.01	objects	39
+280	plastic container	plastic container	5	40	7	container	otherprop	Objects							objects	39
+1229	soap bottle	soap bottle	5	40	7	soap	otherprop	Objects							objects	39
+1230	ikea bag	ikea bag	4	37	7	bag	bag	Objects				2773838	n02773838	bag.n.06	objects	39
+1231	sleeping bag	sleeping bag	4	40	7		otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+1232	duffel bag	duffel bag	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+746	frying pan	frying pan	4	40	7	frying pan	otherprop	Objects					n03400231	frying_pan.n.01	objects	39
+1233	oven mitt	oven mitt	4	40	7		otherprop	Objects							objects	39
+1234	pot	pot	4	40	7	pot	otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+144	hand dryer	hand dryer	4	40	7		otherprop	Objects							objects	39
+282	dollhouse	dollhouse	4	39	6	doll house	otherfurniture	Furniture					n03219483	dollhouse.n.01	objects	39
+167	shampoo bottle	shampoo bottle	4	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1235	hair brush	hair brush	4	40	7		otherprop	Objects					n02908217	brush.n.02	objects	39
+1236	tennis racket	tennis racket	4	40	7		otherprop	Objects					n04409806	tennis_racket.n.01	objects	39
+1237	display case	display case	4	40	7	case	otherprop	Objects							objects	39
+234	ping pong table	ping pong table	4	39	6	ping pong table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+563	boiler	boiler	4	40	7		otherprop	Objects							misc	40
+1238	bag of coffee beans	bag of coffee beans	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+1239	bananas	banana	4	40	7		otherprop	Objects					n00021265	food.n.01	objects	39
+1240	carseat	carseat	4	40	7		otherprop	Objects							misc	40
+366	helmet	helmet	4	40	7		otherprop	Objects			helmet	3513137	n03513137	helmet.n.02	clothes	38
+816	umbrella	umbrella	4	40	7	umbrella	otherprop	Objects					n04507155	umbrella.n.01	objects	39
+1241	coffee box	coffee box	4	40	7		otherprop	Objects							objects	39
+719	envelope	envelope	4	40	7	envelope	otherprop	Objects					n03291819	envelope.n.01	objects	39
+284	wet floor sign	wet floor sign	4	40	7	sign	otherprop	Objects							misc	40
+1242	clothing rack	clothing rack	4	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+247	controller	controller	4	40	7		otherprop	Objects					n03096960	control.n.09	objects	39
+1243	bath walls	bathroom wall	4	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+1244	podium	podium	4	39	6		otherfurniture	Furniture					n03159640	dais.n.01	furniture	36
+1245	storage box	storage box	4	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1246	dolly	dolly	4	40	7		otherprop	Objects							misc	40
+1247	shampoo	shampoo	3	40	7		otherprop	Objects					n04183516	shampoo.n.01	objects	39
+592	paper tray	paper tray	3	40	7	paper tray	otherprop	Objects							objects	39
+385	cabinet door	cabinet door	3	8	12	door	door	Wall	door						door	4
+1248	changing station	changing station	3	40	7		otherprop	Objects							misc	40
+1249	poster printer	poster printer	3	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+133	screen	screen	3	40	7		otherprop	Objects					n03151077	curtain.n.01	curtain	12
+301	soap bar	soap bar	3	38	7	bar	otherstructure	Objects							objects	39
+1250	crutches	crutches	3	40	7		otherprop	Objects					n03141823	crutch.n.01	objects	39
+379	studio light	studio light	3	38	7	light	otherstructure	Objects							lighting	28
+130	stack of cups	cup	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+1251	toilet flush button	toilet flush button	3	40	7		otherprop	Objects							objects	39
+450	trunk	trunk	3	40	7		otherprop	Objects							misc	40
+1252	grocery bag	grocery bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03461288	grocery_bag.n.01	objects	39
+316	plastic bin	plastic bin	3	40	7	bin	otherprop	Objects							objects	39
+1253	pizza box	pizza box	3	29	7	box	box	Objects							objects	39
+385	cabinet doors	cabinet door	3	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	door	4
+1254	legs	legs	3	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+461	car	car	3	40	7	car	otherprop	Objects	car		car	2958343	n02958343	car.n.01	misc	40
+1255	shaving cream	shaving cream	3	40	7		otherprop	Objects					n04186051	shaving_cream.n.01	objects	39
+1256	luggage stand	luggage stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+599	shredder	shredder	3	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+281	statue	statue	3	40	7	sculpture	otherprop	Objects					n04306847	statue.n.01	misc	40
+1257	urinal	urinal	3	33	7	toilet	toilet	Objects	toilet	toilet			n04515991	urinal.n.01	toilet	18
+1258	hose	hose	3	40	7		otherprop	Objects					n03539875	hose.n.03	misc	40
+1259	bike pump	bike pump	3	40	7		otherprop	Objects							objects	39
+319	coatrack	coatrack	3	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1260	bear	bear	3	40	7		otherprop	Objects							objects	39
+28	wall lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1261	humidifier	humidifier	3	40	7		otherprop	Objects							objects	39
+546	toothpaste	toothpaste	3	40	7	toothpaste	otherprop	Objects							objects	39
+1262	mouthwash bottle	mouthwash bottle	3	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1263	poster cutter	poster cutter	3	40	7		otherprop	Objects							objects	39
+1264	golf bag	golf bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03445617	golf_bag.n.01	objects	39
+1265	food container	food container	3	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1266	camera	camera	3	40	7		otherprop	Objects							objects	39
+28	table lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n04380533	table_lamp.n.01	lighting	28
+1267	yoga mat	yoga mat	3	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1268	card	card	3	40	7		otherprop	Objects							objects	39
+1269	mug	mug	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+188	shower doors	shower door	3	38	7		otherstructure	Objects					n04208936	shower.n.01	door	4
+689	cardboard	cardboard	3	40	7		otherprop	Objects							objects	39
+1270	rack stand	rack stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+1271	boxes of paper	boxes of paper	3	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1272	flag	flag	3	40	7		otherprop	Objects							misc	40
+354	futon	futon	3	39	6	mattress	otherfurniture	Furniture					n03408444	futon.n.01	sofa	10
+339	magazine	magazine	3	40	7	magazine	otherprop	Objects					n06595351	magazine.n.01	objects	39
+1009	exit sign	exit sign	3	40	7	exit sign	otherprop	Objects							misc	40
+1273	rolled poster	rolled poster	3	40	7		otherprop	Objects							objects	39
+1274	wheel	wheel	3	40	7		otherprop	Objects							objects	39
+15	pictures	picture	3	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1275	blackboard eraser	blackboard eraser	3	40	7	eraser	otherprop	Objects					n03294833	eraser.n.01	objects	39
+361	organizer	organizer	3	40	7		otherprop	Objects					n03918737	personal_digital_assistant.n.01	objects	39
+1276	doll	doll	3	40	7	toy	otherprop	Objects					n03219135	doll.n.01	objects	39
+326	book rack	book rack	3	39	6	bookrack	otherfurniture	Furniture							objects	39
+1277	laundry bag	laundry bag	3	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+1278	sponge	sponge	3	40	7		otherprop	Objects					n01906749	sponge.n.04	objects	39
+116	seating	seat	3	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+1184	folded chairs	folded chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1279	lotion bottle	lotion bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+212	can	can	2	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1280	lunch box	lunch box	2	40	7		otherprop	Objects							objects	39
+1281	food display	food display	2	40	7		otherprop	Objects							misc	40
+794	storage shelf	storage shelf	2	40	7		otherprop	Objects							shelving	31
+1282	sliding wood door	sliding wood door	2	40	7		otherprop	Objects							door	4
+955	pants	pants	2	40	7		otherprop	Objects					n04489008	trouser.n.01	clothes	38
+387	wood	wood	2	40	7		otherprop	Objects							misc	40
+69	boards	board	2	38	7	board	otherstructure	Objects							board_panel	35
+65	bottles	bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+523	washcloth	washcloth	2	40	7		otherprop	Objects					n04554523	washcloth.n.01	towel	20
+389	workbench	workbench	2	39	6	bench	otherfurniture	Furniture	bench		table	4379243	n04600486	workbench.n.01	table	5
+29	open kitchen cabinet	kitchen cabinet	2	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+1283	organizer shelf	organizer shelf	2	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+146	frame	frame	2	38	7		otherstructure	Objects							misc	40
+130	cups	cup	2	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+372	exercise ball	exercise ball	2	40	7	ball	otherprop	Objects					n04285146	sports_equipment.n.01	gym_equipment	33
+289	easel	easel	2	39	6	stand	otherfurniture	Furniture					n03262809	easel.n.01	furniture	36
+440	garbage bag	garbage bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+321	roomba	roomba	2	40	7		otherprop	Objects							objects	39
+976	garage door	garage door	2	38	7	garage door	otherstructure	Objects	door						door	4
+1256	luggage rack	luggage stand	2	39	6	stand	otherfurniture	Furniture					n04038440		shelving	31
+1284	bike lock	bike lock	2	40	7		otherprop	Objects							objects	39
+1285	briefcase	briefcase	2	40	7		otherprop	Objects					n02900705	briefcase.n.01	objects	39
+357	hand towel	hand towel	2	27	7	towel	towel	Objects					n03490006	hand_towel.n.01	towel	20
+1286	bath products	bath product	2	40	7		otherprop	Objects							objects	39
+1287	star	star	2	40	7		otherprop	Objects					n09444783	star.n.03	misc	40
+365	map	map	2	40	7	map	otherprop	Objects					n03720163	map.n.01	misc	40
+1288	coffee bean bag	coffee bean bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+81	headboard	headboard	2	39	6	headboard	otherfurniture	Furniture					n03502200	headboard.n.01	bed	11
+1289	ipad	ipad	2	40	7		otherprop	Objects							objects	39
+1290	display rack	display rack	2	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+948	traffic cone	traffic cone	2	40	7	cone	otherprop	Objects	cone						objects	39
+174	toiletry	toiletry	2	40	7		otherprop	Objects					n04447443	toiletry.n.01	objects	39
+1028	canopy	canopy	2	40	7		otherprop	Objects							misc	40
+1291	massage chair	massage chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1292	paper organizer	paper organizer	2	40	7		otherprop	Objects							objects	39
+1005	barricade	barricade	2	40	7		otherprop	Objects							misc	40
+235	platform	platform	2	38	7		otherstructure	Objects							misc	40
+1293	cap	cap	2	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+1294	dumbbell plates	dumbbell plates	2	40	7		otherprop	Objects							objects	39
+1295	elevator	elevator	2	38	7		otherstructure	Objects							misc	40
+1296	cooking pan	cooking pan	2	40	7	pan	otherprop	Objects					n03880531	pan.n.01	objects	39
+1297	trash bag	trash bag	2	37	7	bag	bag	Objects							objects	39
+1298	santa	santa	2	40	7		otherprop	Objects							misc	40
+1299	jewelry box	jewelry box	2	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1300	boat	boat	2	40	7		otherprop	Objects							misc	40
+1301	sock	sock	2	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1051	kinect	kinect	2	40	7	kinect	otherprop	Objects							objects	39
+566	crib	crib	2	39	6	crib	otherfurniture	Furniture							furniture	36
+1302	plastic storage bin	plastic storage bin	2	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1062	cooler	cooler	2	24	6	refridgerator	refridgerator	Furniture					n03102654	cooler.n.01	appliances	37
+1303	kitchen apron	kitchen apron	2	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1304	dishwashing soap bottle	dishwashing soap bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1305	xbox controller	xbox controller	2	40	7		otherprop	Objects							objects	39
+1306	banana holder	banana holder	2	40	7		otherprop	Objects							objects	39
+298	ping pong paddle	ping pong paddle	2	40	7		otherprop	Objects							table	5
+1307	airplane	airplane	2	40	7		otherprop	Objects							misc	40
+1308	conditioner bottle	conditioner bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1309	tea kettle	tea kettle	2	40	7	tea kettle	otherprop	Objects					n04397768	teakettle.n.01	objects	39
+43	bedframe	bedframe	2	39	6		otherfurniture	Furniture					n02822579	bedstead.n.01	bed	11
+1310	wood beam	wood beam	2	38	7		otherstructure	Objects							beam	29
+593	toilet paper package	toilet paper package	2	40	7		otherprop	Objects							objects	39
+1311	wall mounted coat rack	wall mounted coat rack	2	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1312	film light	film light	2	40	7		otherprop	Objects							lighting	28
+749	ceiling lamp	ceiling lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+623	chain	chain	1	40	7		otherprop	Objects							chair	3
+1313	sofa	sofa	1	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+99	closet wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+265	sweater	sweater	1	40	7		otherprop	Objects					n04370048	sweater.n.01	clothes	38
+1314	kitchen mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+99	wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1315	water softener	water softener	1	40	7		otherprop	Objects							misc	40
+448	banister	banister	1	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+257	trolley	trolley	1	40	7	trolley	otherprop	Objects					n04335435	streetcar.n.01	misc	40
+1316	pantry shelf	pantry shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+786	sofa bed	sofa bed	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+801	loofa	loofa	1	40	7		otherprop	Objects							objects	39
+972	shower faucet handle	shower faucet handle	1	40	7	handle	otherprop	Objects							shower	23
+1317	toy piano	toy piano	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1318	fish	fish	1	40	7		otherprop	Objects					n02512053	fish.n.01	objects	39
+75	file cabinets	file cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n03337140	file.n.03	cabinet	7
+657	cat litter box	cat litter box	1	29	7	box	box	Objects							objects	39
+561	electric panel	electric panel	1	40	7		otherprop	Objects							misc	40
+93	suitcases	suitcase	1	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+513	curtain rod	curtain rod	1	38	7	curtain rod	otherstructure	Objects							curtain	12
+411	bunk bed	bunk bed	1	39	6	bunk bed	otherfurniture	Furniture	bed	bed	bed	2818832	n02920259	bunk_bed.n.01	bed	11
+1122	chandelier	chandelier	1	38	7	chandelier	otherstructure	Objects					n03005285	chandelier.n.01	lighting	28
+922	tape	tape	1	40	7	tape	otherprop	Objects							objects	39
+88	plates	plate	1	40	7		otherprop	Objects					n03959485	plate.n.04	objects	39
+518	alarm	alarm	1	40	7	alarm	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+814	fire hose	fire hose	1	40	7		otherprop	Objects					n03346004	fire_hose.n.01	misc	40
+1319	toy dinosaur	toy dinosaur	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1320	cone	cone	1	40	7		otherprop	Objects							objects	39
+649	glass doors	glass door	1	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+607	hatrack	hatrack	1	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+819	subwoofer	subwoofer	1	40	7	speaker	otherprop	Objects			speaker	3691459	n04349401	subwoofer.n.01	objects	39
+1321	fire sprinkler	fire sprinkler	1	40	7		otherprop	Objects							misc	40
+1322	trash cabinet	trash cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+1204	pantry walls	pantry wall	1	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+227	photo	photo	1	40	7	photo	otherprop	Objects					n03925226	photograph.n.01	picture	6
+817	barrier	barrier	1	40	7		otherprop	Objects					n02796623	barrier.n.01	misc	40
+130	stacks of cups	cup	1	40	7		otherprop	Objects					n03147509	cup.n.01	objects	39
+712	beachball	beachball	1	40	7	ball	otherprop	Objects					n02814224	beach_ball.n.01	objects	39
+1323	folded boxes	folded boxes	1	40	7		otherprop	Objects							objects	39
+1324	contact lens solution bottle	contact lens solution bottle	1	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+673	covered box	covered box	1	29	7	box	box	Objects							objects	39
+459	folder	folder	1	40	7	folder	otherprop	Objects					n03376279	folder.n.02	objects	39
+643	mail trays	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+238	slipper	slipper	1	40	7		otherprop	Objects					n04241394	slipper.n.01	clothes	38
+765	magazine rack	magazine rack	1	39	6	stand	otherfurniture	Furniture					n03704549	magazine_rack.n.01	shelving	31
+1008	sticker	sticker	1	40	7	sticker	otherprop	Objects					n07272545	gummed_label.n.01	objects	39
+225	lotion	lotion	1	40	7		otherprop	Objects					n03690938	lotion.n.01	objects	39
+1083	buddha	buddha	1	40	7		otherprop	Objects							objects	39
+813	file organizer	file organizer	1	40	7		otherprop	Objects							objects	39
+138	paper towel rolls	paper towel roll	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+1145	night lamp	night lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+796	fuse box	fuse box	1	40	7		otherprop	Objects							misc	40
+1325	knife block	knife block	1	40	7		otherprop	Objects							objects	39
+363	furnace	furnace	1	39	6	furnace	otherfurniture	Furniture					n03404449	furnace.n.01
+1174	cd cases	cd case	1	40	7		otherprop	Objects							objects	39
+38	stools	stool	1	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+1326	hand sanitzer dispenser	hand sanitzer dispenser	1	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+997	teapot	teapot	1	40	7	tea pot	otherprop	Objects					n04398044	teapot.n.01	objects	39
+1327	pen holder	pen holder	1	40	7		otherprop	Objects							objects	39
+1328	tray rack	tray rack	1	40	7		otherprop	Objects							objects	39
+1329	wig	wig	1	40	7		otherprop	Objects					n04584207	wig.n.01	objects	39
+182	switch	switch	1	40	7		otherprop	Objects					n04372370	switch.n.01	misc	40
+280	plastic containers	plastic container	1	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1330	night light	night light	1	40	7		otherprop	Objects							lighting	28
+1331	notepad	notepad	1	40	7		otherprop	Objects							objects	39
+1332	mail bin	mail bin	1	40	7		otherprop	Objects							misc	40
+1333	elevator button	elevator button	1	40	7		otherprop	Objects							misc	40
+939	gaming wheel	gaming wheel	1	40	7		otherprop	Objects							objects	39
+1334	drum set	drum set	1	40	7		otherprop	Objects							objects	39
+480	cosmetic bag	cosmetic bag	1	37	7	bag	bag	Objects							objects	39
+907	coffee mug	coffee mug	1	40	7	vessel	otherprop	Objects			cup or mug	3797390	n03063599	coffee_mug.n.01	objects	39
+1335	closet shelf	closet shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+1336	baby mobile	baby mobile	1	40	7		otherprop	Objects							objects	39
+829	diaper bin	diaper bin	1	40	7	bin	otherprop	Objects							objects	39
+947	door wall	door wall	1	1	12	wall	wall	Wall							wall	1
+1116	stepstool	stepstool	1	40	7	step stool	otherprop	Objects							objects	39
+599	paper shredder	shredder	1	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+733	dress rack	dress rack	1	40	7		otherprop	Objects					n03238762	dress_rack.n.01	misc	40
+123	cover	cover	1	40	7	blanket	otherprop	Objects							objects	39
+506	shopping bag	shopping bag	1	37	7	bag	bag	Objects					n04204081	shopping_bag.n.01	objects	39
+569	sliding door	sliding door	1	8	12	door	door	Wall	door				n04239074	sliding_door.n.01	door	4
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+332	mop	mop	1	40	7		otherprop	Objects					n04367480	swab.n.02	objects	39
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+1355	air hockey table	air hockey table	1	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
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+1357	starbucks cup	starbucks cup	1	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
--- a/data/scannet/meta_data/scannetv2_test.txt
+++ b/data/scannet/meta_data/scannetv2_test.txt
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--- a/data/scannet/meta_data/scannetv2_train.txt
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--- a/data/scannet/meta_data/scannetv2_val.txt
+++ b/data/scannet/meta_data/scannetv2_val.txt
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--- a/data/scannet/scannet_utils.py
+++ b/data/scannet/scannet_utils.py
+# Modified from
+# https://github.com/facebookresearch/votenet/blob/master/scannet/scannet_utils.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""Ref: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts
+"""
+import csv
+import os
+import numpy as np
+from plyfile import PlyData
+def represents_int(s):
+    """Judge whether string s represents an int.
+    Args:
+        s(str): The input string to be judged.
+    Returns:
+        bool: Whether s represents int or not.
+    """
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+def read_label_mapping(filename,
+                       label_from='raw_category',
+                       label_to='nyu40id'):
+    assert os.path.isfile(filename)
+    mapping = dict()
+    with open(filename) as csvfile:
+        reader = csv.DictReader(csvfile, delimiter='\t')
+        for row in reader:
+            mapping[row[label_from]] = int(row[label_to])
+    if represents_int(list(mapping.keys())[0]):
+        mapping = {int(k): v for k, v in mapping.items()}
+    return mapping
+def read_mesh_vertices(filename):
+    """Read XYZ for each vertex.
+    Args:
+        filename(str): The name of the mesh vertices file.
+    Returns:
+        ndarray: Vertices.
+    """
+    assert os.path.isfile(filename)
+    with open(filename, 'rb') as f:
+        plydata = PlyData.read(f)
+        num_verts = plydata['vertex'].count
+        vertices = np.zeros(shape=[num_verts, 3], dtype=np.float32)
+        vertices[:, 0] = plydata['vertex'].data['x']
+        vertices[:, 1] = plydata['vertex'].data['y']
+        vertices[:, 2] = plydata['vertex'].data['z']
+    return vertices
+def read_mesh_vertices_rgb(filename):
+    """Read XYZ and RGB for each vertex.
+    Args:
+        filename(str): The name of the mesh vertices file.
+    Returns:
+        Vertices. Note that RGB values are in 0-255.
+    """
+    assert os.path.isfile(filename)
+    with open(filename, 'rb') as f:
+        plydata = PlyData.read(f)
+        num_verts = plydata['vertex'].count
+        vertices = np.zeros(shape=[num_verts, 6], dtype=np.float32)
+        vertices[:, 0] = plydata['vertex'].data['x']
+        vertices[:, 1] = plydata['vertex'].data['y']
+        vertices[:, 2] = plydata['vertex'].data['z']
+        vertices[:, 3] = plydata['vertex'].data['red']
+        vertices[:, 4] = plydata['vertex'].data['green']
+        vertices[:, 5] = plydata['vertex'].data['blue']
+    return vertices
--- a/data/sunrgbd/README.md
+++ b/data/sunrgbd/README.md
+### Prepare SUN RGB-D Data
+We follow the procedure in [votenet](https://github.com/facebookresearch/votenet/).
+1. Download SUNRGBD v2 data [HERE](http://rgbd.cs.princeton.edu/data/). Then, move SUNRGBD.zip, SUNRGBDMeta2DBB_v2.mat, SUNRGBDMeta3DBB_v2.mat and SUNRGBDtoolbox.zip to the OFFICIAL_SUNRGBD folder, unzip the zip files.
+2. Enter the `matlab` folder, Extract point clouds and annotations by running `extract_split.m`, `extract_rgbd_data_v2.m` and `extract_rgbd_data_v1.m`.
+3. Back to this directory, prepare data by running `python sunrgbd_data.py --gen_v1_data`.
+4. Enter the project root directory, Generate training data by running
+```bash
+python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd
+```
+The overall process could be achieved through the following script
+```bash
+cd matlab
+matlab -nosplash -nodesktop -r 'extract_split;quit;'
+matlab -nosplash -nodesktop -r 'extract_rgbd_data_v2;quit;'
+matlab -nosplash -nodesktop -r 'extract_rgbd_data_v1;quit;'
+cd ..
+python sunrgbd_data.py --gen_v1_data
+cd ..
+python tools/create_data.py sunrgbd --root-path ./data/sunrgbd --out-dir ./data/sunrgbd --extra-tag sunrgbd
+```
+NOTE: SUNRGBDtoolbox.zip should have MD5 hash `18d22e1761d36352f37232cba102f91f` (you can check the hash with `md5 SUNRGBDtoolbox.zip` on Mac OS or `md5sum SUNRGBDtoolbox.zip` on Linux)
+The directory structure after pre-processing should be as below
+```
+sunrgbd
+├── sunrgbd_utils.py
+├── sunrgbd_data.py
+├── README.md
+├── matlab
+│   ├── extract_rgbd_data_v1.m
+│   ├── extract_rgbd_data_v2.m
+│   ├── extract_split.m
+├── OFFICIAL_SUNRGBD
+│   ├── SUNRGBD
+│   ├── SUNRGBDMeta2DBB_v2.mat
+│   ├── SUNRGBDMeta3DBB_v2.mat
+│   ├── SUNRGBDtoolbox
+├── sunrgbd_trainval
+│   ├── calib
+│   ├── image
+│   ├── label_v1
+│   ├── train_data_idx.txt
+│   ├── depth
+│   ├── label
+│   ├── seg_label
+│   ├── val_data_idx.txt
+├── sunrgbd_pc_bbox_votes_50k_v1_train
+├── sunrgbd_pc_bbox_votes_50k_v1_val
+├── points
+├── sunrgbd_infos_train.pkl
+├── sunrgbd_infos_val.pkl
+```
--- a/data/sunrgbd/matlab/extract_rgbd_data_v1.m
+++ b/data/sunrgbd/matlab/extract_rgbd_data_v1.m
+% Copyright (c) Facebook, Inc. and its affiliates.
+%
+% This source code is licensed under the MIT license found in the
+% LICENSE file in the root directory of this source tree.
+%% Dump SUNRGBD data to our format
+% for each sample, we have RGB image, 2d boxes.
+% point cloud (in camera coordinate), calibration and 3d boxes.
+%
+% Extract using V1 labels.
+%
+% Author: Charles R. Qi
+%
+clear; close all; clc;
+addpath(genpath('.'))
+addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox')
+%% V1 2D&3D BB and Seg masks
+load('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/Metadata/SUNRGBDMeta.mat')
+% load('./Metadata/SUNRGBD2Dseg.mat')
+%% Create folders
+det_label_folder = '../sunrgbd_trainval/label_v1/';
+mkdir(det_label_folder);
+%% Read
+for imageId = 1:10335
+    imageId
+try
+data = SUNRGBDMeta(imageId);
+data.depthpath(1:16) = '';
+data.depthpath = strcat('../OFFICIAL_SUNRGBD/SUNRGBD', data.depthpath);
+data.rgbpath(1:16) = '';
+data.rgbpath = strcat('../OFFICIAL_SUNRGBD/SUNRGBD', data.rgbpath);
+% MAT files are 3x smaller than TXT files. In Python we can use
+% scipy.io.loadmat('xxx.mat')['points3d_rgb'] to load the data.
+mat_filename = strcat(num2str(imageId,'%06d'), '.mat');
+txt_filename = strcat(num2str(imageId,'%06d'), '.txt');
+% Write 2D and 3D box label
+data2d = data;
+fid = fopen(strcat(det_label_folder, txt_filename), 'w');
+for j = 1:length(data.groundtruth3DBB)
+    centroid = data.groundtruth3DBB(j).centroid;
+    classname = data.groundtruth3DBB(j).classname;
+    orientation = data.groundtruth3DBB(j).orientation;
+    coeffs = abs(data.groundtruth3DBB(j).coeffs);
+    box2d = data2d.groundtruth2DBB(j).gtBb2D;
+    fprintf(fid, '%s %d %d %d %d %f %f %f %f %f %f %f %f\n', classname, box2d(1), box2d(2), box2d(3), box2d(4), centroid(1), centroid(2), centroid(3), coeffs(1), coeffs(2), coeffs(3), orientation(1), orientation(2));
+end
+fclose(fid);
+catch
+end
+end
--- a/data/sunrgbd/matlab/extract_rgbd_data_v2.m
+++ b/data/sunrgbd/matlab/extract_rgbd_data_v2.m
+% Copyright (c) Facebook, Inc. and its affiliates.
+%
+% This source code is licensed under the MIT license found in the
+% LICENSE file in the root directory of this source tree.
+%% Dump SUNRGBD data to our format
+% for each sample, we have RGB image, 2d boxes.
+% point cloud (in camera coordinate), calibration and 3d boxes.
+%
+% Compared to extract_rgbd_data.m in frustum_pointents, use v2 2D and 3D
+% bboxes.
+%
+% Author: Charles R. Qi
+%
+clear; close all; clc;
+addpath(genpath('.'))
+addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/readData')
+%% V1 2D&3D BB and Seg masks
+% load('./Metadata/SUNRGBDMeta.mat')
+% load('./Metadata/SUNRGBD2Dseg.mat')
+%% V2 3DBB annotations (overwrites SUNRGBDMeta)
+load('../OFFICIAL_SUNRGBD/SUNRGBDMeta3DBB_v2.mat');
+load('../OFFICIAL_SUNRGBD/SUNRGBDMeta2DBB_v2.mat');
+%% Create folders
+depth_folder = '../sunrgbd_trainval/depth/';
+image_folder = '../sunrgbd_trainval/image/';
+calib_folder = '../sunrgbd_trainval/calib/';
+det_label_folder = '../sunrgbd_trainval/label/';
+seg_label_folder = '../sunrgbd_trainval/seg_label/';
+mkdir(depth_folder);
+mkdir(image_folder);
+mkdir(calib_folder);
+mkdir(det_label_folder);
+mkdir(seg_label_folder);
+%% Read
+parfor imageId = 1:10335
+    imageId
+try
+data = SUNRGBDMeta(imageId);
+data.depthpath(1:16) = '';
+data.depthpath = strcat('../OFFICIAL_SUNRGBD', data.depthpath);
+data.rgbpath(1:16) = '';
+data.rgbpath = strcat('../OFFICIAL_SUNRGBD', data.rgbpath);
+% Write point cloud in depth map
+[rgb,points3d,depthInpaint,imsize]=read3dPoints(data);
+rgb(isnan(points3d(:,1)),:) = [];
+points3d(isnan(points3d(:,1)),:) = [];
+points3d_rgb = [points3d, rgb];
+% MAT files are 3x smaller than TXT files. In Python we can use
+% scipy.io.loadmat('xxx.mat')['points3d_rgb'] to load the data.
+mat_filename = strcat(num2str(imageId,'%06d'), '.mat');
+txt_filename = strcat(num2str(imageId,'%06d'), '.txt');
+parsave(strcat(depth_folder, mat_filename), points3d_rgb);
+% Write images
+copyfile(data.rgbpath, sprintf('%s/%06d.jpg', image_folder, imageId));
+% Write calibration
+dlmwrite(strcat(calib_folder, txt_filename), data.Rtilt(:)', 'delimiter', ' ');
+dlmwrite(strcat(calib_folder, txt_filename), data.K(:)', 'delimiter', ' ', '-append');
+% Write 2D and 3D box label
+data2d = SUNRGBDMeta2DBB(imageId);
+fid = fopen(strcat(det_label_folder, txt_filename), 'w');
+for j = 1:length(data.groundtruth3DBB)
+    centroid = data.groundtruth3DBB(j).centroid;
+    classname = data.groundtruth3DBB(j).classname;
+    orientation = data.groundtruth3DBB(j).orientation;
+    coeffs = abs(data.groundtruth3DBB(j).coeffs);
+    box2d = data2d.groundtruth2DBB(j).gtBb2D;
+    assert(strcmp(data2d.groundtruth2DBB(j).classname, classname));
+    fprintf(fid, '%s %d %d %d %d %f %f %f %f %f %f %f %f\n', classname, box2d(1), box2d(2), box2d(3), box2d(4), centroid(1), centroid(2), centroid(3), coeffs(1), coeffs(2), coeffs(3), orientation(1), orientation(2));
+end
+fclose(fid);
+catch
+end
+end
+function parsave(filename, instance)
+save(filename, 'instance');
+end
--- a/data/sunrgbd/matlab/extract_split.m
+++ b/data/sunrgbd/matlab/extract_split.m
+% Modified from
+% https://github.com/facebookresearch/votenet/blob/master/sunrgbd/matlab/extract_split.m
+% Copyright (c) Facebook, Inc. and its affiliates.
+%
+% This source code is licensed under the MIT license found in the
+% LICENSE file in the root directory of this source tree.
+%% Dump train/val split.
+% Author: Charles R. Qi
+addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox')
+%% Construct Hash Map
+hash_train = java.util.Hashtable;
+hash_val = java.util.Hashtable;
+split = load('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/traintestSUNRGBD/allsplit.mat');
+N_train = length(split.alltrain);
+N_val = length(split.alltest);
+for i = 1:N_train
+    folder_path = split.alltrain{i};
+    folder_path(1:16) = '';
+    hash_train.put(folder_path,0);
+end
+for i = 1:N_val
+    folder_path = split.alltest{i};
+    folder_path(1:16) = '';
+    hash_val.put(folder_path,0);
+end
+%% Map data to train or val set.
+load('../OFFICIAL_SUNRGBD/SUNRGBDMeta3DBB_v2.mat');
+if exist('../sunrgbd_trainval','dir')==0
+    mkdir('../sunrgbd_trainval');
+end
+fid_train = fopen('../sunrgbd_trainval/train_data_idx.txt', 'w');
+fid_val = fopen('../sunrgbd_trainval/val_data_idx.txt', 'w');
+for imageId = 1:10335
+    data = SUNRGBDMeta(imageId);
+    depthpath = data.depthpath;
+    depthpath(1:16) = '';
+    [filepath,name,ext] = fileparts(depthpath);
+    [filepath,name,ext] = fileparts(filepath);
+    if hash_train.containsKey(filepath)
+        fprintf(fid_train, '%d\n', imageId);
+    elseif hash_val.containsKey(filepath)
+        fprintf(fid_val, '%d\n', imageId);
+    else
+        a = 1;
+    end
+end
+fclose(fid_train);
+fclose(fid_val);
--- a/data/sunrgbd/sunrgbd_data.py
+++ b/data/sunrgbd/sunrgbd_data.py
+# Modified from
+# https://github.com/facebookresearch/votenet/blob/master/sunrgbd/sunrgbd_data.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+''' Helper class and functions for loading SUN RGB-D objects
+Author: Charles R. Qi
+Date: December, 2018
+Note: removed unused code for frustum preparation.
+Changed a way for data visualization (removed depdency on mayavi).
+Load depth with scipy.io
+'''
+import argparse
+import os
+import sys
+import numpy as np
+import sunrgbd_utils
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, '../utils/'))
+DEFAULT_TYPE_WHITELIST = [
+    'bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
+    'night_stand', 'bookshelf', 'bathtub'
+]
+def random_sampling(pc, num_sample, replace=None, return_choices=False):
+    """ Input is NxC, output is num_samplexC
+    """
+    if replace is None:
+        replace = (pc.shape[0] < num_sample)
+    choices = np.random.choice(pc.shape[0], num_sample, replace=replace)
+    if return_choices:
+        return pc[choices], choices
+    else:
+        return pc[choices]
+class sunrgbd_object(object):
+    """Surrgbd object.
+    Load and parse object data.
+    Args:
+        root_dir(str): Root directory.
+        split(str): Training or testing.
+            Default: 'training.
+        use_v1(bool): Whether to use v1.
+            Default(False).
+    """
+    def __init__(self, root_dir, split='training', use_v1=False):
+        self.root_dir = root_dir
+        self.split = split
+        assert (self.split == 'training')
+        self.split_dir = os.path.join(root_dir)
+        if split == 'training':
+            self.num_samples = 10335
+        elif split == 'testing':
+            self.num_samples = 2860
+        else:
+            print('Unknown split: %s' % (split))
+            exit(-1)
+        self.image_dir = os.path.join(self.split_dir, 'image')
+        self.calib_dir = os.path.join(self.split_dir, 'calib')
+        self.depth_dir = os.path.join(self.split_dir, 'depth')
+        if use_v1:
+            self.label_dir = os.path.join(self.split_dir, 'label_v1')
+        else:
+            self.label_dir = os.path.join(self.split_dir, 'label')
+    def __len__(self):
+        return self.num_samples
+    def get_image(self, idx):
+        img_filename = os.path.join(self.image_dir, '%06d.jpg' % (idx))
+        return sunrgbd_utils.load_image(img_filename)
+    def get_depth(self, idx):
+        depth_filename = os.path.join(self.depth_dir, '%06d.mat' % (idx))
+        return sunrgbd_utils.load_depth_points_mat(depth_filename)
+    def get_calibration(self, idx):
+        calib_filename = os.path.join(self.calib_dir, '%06d.txt' % (idx))
+        return sunrgbd_utils.SUNRGBD_Calibration(calib_filename)
+    def get_label_objects(self, idx):
+        label_filename = os.path.join(self.label_dir, '%06d.txt' % (idx))
+        return sunrgbd_utils.read_sunrgbd_label(label_filename)
+def extract_sunrgbd_data(idx_filename,
+                         split,
+                         output_folder,
+                         num_point=20000,
+                         type_whitelist=DEFAULT_TYPE_WHITELIST,
+                         save_votes=False,
+                         use_v1=False,
+                         skip_empty_scene=True):
+    """Extract scene point clouds and
+            bounding boxes (centroids, box sizes, heading angles,
+            semantic classes). Dumped point clouds and boxes are in
+            upright depth coord.
+    Args:
+        idx_filename: a TXT file where each line is an int number (index)
+        split: training or testing
+        save_votes: whether to compute and save Ground truth votes.
+        use_v1: use the SUN RGB-D V1 data
+        skip_empty_scene: if True, skip scenes that contain no object
+        (no objet in whitelist)
+    Dumps:
+        <id>_pc.npz of (N,6) where N is for number of subsampled points
+            and 6 is for XYZ and RGB (in 0~1) in upright depth coord
+        <id>_bbox.npy of (K,8) where K is the number of objects, 8 is for
+            centroids (cx,cy,cz), dimension (l,w,h), heanding_angle and
+            semantic_class
+        <id>_votes.npz of (N,10) with 0/1 indicating whether the point
+            belongs to an object, then three sets of GT votes for up to
+            three objects. If the point is only in one object's OBB, then
+            the three GT votes are the same.
+    """
+    dataset = sunrgbd_object('./sunrgbd_trainval', split, use_v1=use_v1)
+    data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
+    if not os.path.exists(output_folder):
+        os.mkdir(output_folder)
+    for data_idx in data_idx_list:
+        print('------------- ', data_idx)
+        objects = dataset.get_label_objects(data_idx)
+        if skip_empty_scene and \
+                (len(objects) == 0 or
+                 len([obj for obj in objects if
+                      obj.classname in type_whitelist]) == 0):
+            continue
+        object_list = []
+        for obj in objects:
+            if obj.classname not in type_whitelist:
+                continue
+            obb = np.zeros((8))
+            obb[0:3] = obj.centroid
+            obb[3:6] = np.array([obj.length, obj.width, obj.height])
+            obb[6] = obj.heading_angle
+            obb[7] = sunrgbd_utils.type2class[obj.classname]
+            object_list.append(obb)
+        if len(object_list) == 0:
+            obbs = np.zeros((0, 8))
+        else:
+            obbs = np.vstack(object_list)  # (K,8)
+        pc_upright_depth = dataset.get_depth(data_idx)
+        pc_upright_depth_subsampled = random_sampling(pc_upright_depth,
+                                                      num_point)
+        np.savez_compressed(
+            os.path.join(output_folder, '%06d_pc.npz' % (data_idx)),
+            pc=pc_upright_depth_subsampled)
+        np.save(
+            os.path.join(output_folder, '%06d_bbox.npy' % (data_idx)), obbs)
+        if save_votes:
+            N = pc_upright_depth_subsampled.shape[0]
+            point_votes = np.zeros((N, 10))  # 3 votes and 1 vote mask
+            point_vote_idx = np.zeros(
+                (N)).astype(np.int32)  # in the range of [0,2]
+            indices = np.arange(N)
+            for obj in objects:
+                if obj.classname not in type_whitelist:
+                    continue
+                try:
+                    # Find all points in this object's OBB
+                    box3d_pts_3d = sunrgbd_utils.my_compute_box_3d(
+                        obj.centroid,
+                        np.array([obj.length, obj.width, obj.height]),
+                        obj.heading_angle)
+                    pc_in_box3d, inds = sunrgbd_utils.extract_pc_in_box3d(
+                        pc_upright_depth_subsampled, box3d_pts_3d)
+                    point_votes[inds, 0] = 1
+                    votes = np.expand_dims(obj.centroid, 0) - pc_in_box3d[:,
+                                                                          0:3]
+                    sparse_inds = indices[inds]
+                    for i in range(len(sparse_inds)):
+                        j = sparse_inds[i]
+                        point_votes[j,
+                                    int(point_vote_idx[j] * 3 +
+                                        1):int((point_vote_idx[j] + 1) * 3 +
+                                               1)] = votes[i, :]
+                        # Populate votes with the fisrt vote
+                        if point_vote_idx[j] == 0:
+                            point_votes[j, 4:7] = votes[i, :]
+                            point_votes[j, 7:10] = votes[i, :]
+                    point_vote_idx[inds] = np.minimum(2,
+                                                      point_vote_idx[inds] + 1)
+                except Exception:
+                    print('ERROR ----', data_idx, obj.classname)
+            np.savez_compressed(
+                os.path.join(output_folder, '%06d_votes.npz' % (data_idx)),
+                point_votes=point_votes)
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--compute_median_size',
+        action='store_true',
+        help='Compute median 3D bounding box sizes for each class.')
+    parser.add_argument(
+        '--gen_v1_data', action='store_true', help='Generate V1 dataset.')
+    parser.add_argument(
+        '--gen_v2_data', action='store_true', help='Generate V2 dataset.')
+    args = parser.parse_args()
+    if args.gen_v1_data:
+        extract_sunrgbd_data(
+            os.path.join(BASE_DIR, 'sunrgbd_trainval/train_data_idx.txt'),
+            split='training',
+            output_folder=os.path.join(BASE_DIR,
+                                       'sunrgbd_pc_bbox_votes_50k_v1_train'),
+            save_votes=True,
+            num_point=50000,
+            use_v1=True,
+            skip_empty_scene=False)
+        extract_sunrgbd_data(
+            os.path.join(BASE_DIR, 'sunrgbd_trainval/val_data_idx.txt'),
+            split='training',
+            output_folder=os.path.join(BASE_DIR,
+                                       'sunrgbd_pc_bbox_votes_50k_v1_val'),
+            save_votes=True,
+            num_point=50000,
+            use_v1=True,
+            skip_empty_scene=False)
+    if args.gen_v2_data:
+        extract_sunrgbd_data(
+            os.path.join(BASE_DIR, 'sunrgbd_trainval/train_data_idx.txt'),
+            split='training',
+            output_folder=os.path.join(BASE_DIR,
+                                       'sunrgbd_pc_bbox_votes_50k_v2_train'),
+            save_votes=True,
+            num_point=50000,
+            use_v1=False,
+            skip_empty_scene=False)
+        extract_sunrgbd_data(
+            os.path.join(BASE_DIR, 'sunrgbd_trainval/val_data_idx.txt'),
+            split='training',
+            output_folder=os.path.join(BASE_DIR,
+                                       'sunrgbd_pc_bbox_votes_50k_v2_val'),
+            save_votes=True,
+            num_point=50000,
+            use_v1=False,
+            skip_empty_scene=False)
--- a/data/sunrgbd/sunrgbd_utils.py
+++ b/data/sunrgbd/sunrgbd_utils.py
+# Modified from
+# https://github.com/facebookresearch/votenet/blob/master/sunrgbd/sunrgbd_utils.py
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""Provides Python helper function to read My SUNRGBD dataset.
+Author: Charles R. Qi
+Date: October, 2017
+Updated by Charles R. Qi
+Date: December, 2018
+Note: removed basis loading.
+"""
+import cv2
+import numpy as np
+import scipy.io as sio
+type2class = {
+    'bed': 0,
+    'table': 1,
+    'sofa': 2,
+    'chair': 3,
+    'toilet': 4,
+    'desk': 5,
+    'dresser': 6,
+    'night_stand': 7,
+    'bookshelf': 8,
+    'bathtub': 9
+}
+class2type = {type2class[t]: t for t in type2class}
+def flip_axis_to_camera(pc):
+    """Flip axis to camera.
+    Flip X-right,Y-forward,Z-up to X-right,Y-down,Z-forward.
+    Args:
+        pc(ndarray): points in depth axis.
+    Returns:
+        ndarray: points in camera  axis.
+    """
+    pc2 = np.copy(pc)
+    pc2[:, [0, 1, 2]] = pc2[:, [0, 2, 1]]  # cam X,Y,Z = depth X,-Z,Y
+    pc2[:, 1] *= -1
+    return pc2
+def flip_axis_to_depth(pc):
+    pc2 = np.copy(pc)
+    pc2[:, [0, 1, 2]] = pc2[:, [0, 2, 1]]  # depth X,Y,Z = cam X,Z,-Y
+    pc2[:, 2] *= -1
+    return pc2
+class SUNObject3d(object):
+    def __init__(self, line):
+        data = line.split(' ')
+        data[1:] = [float(x) for x in data[1:]]
+        self.classname = data[0]
+        self.xmin = data[1]
+        self.ymin = data[2]
+        self.xmax = data[1] + data[3]
+        self.ymax = data[2] + data[4]
+        self.box2d = np.array([self.xmin, self.ymin, self.xmax, self.ymax])
+        self.centroid = np.array([data[5], data[6], data[7]])
+        self.unused_dimension = np.array([data[8], data[9], data[10]])
+        self.width = data[8]
+        self.length = data[9]
+        self.height = data[10]
+        self.orientation = np.zeros((3, ))
+        self.orientation[0] = data[11]
+        self.orientation[1] = data[12]
+        self.heading_angle = -1 * np.arctan2(self.orientation[1],
+                                             self.orientation[0])
+class SUNRGBD_Calibration(object):
+    """Calibration matrices and utils
+    We define five coordinate system in SUN RGBD dataset:
+        camera coodinate:
+            Z is forward, Y is downward, X is rightward.
+        depth coordinate:
+            Just change axis order and flip up-down axis from camera coord.
+        upright depth coordinate: tilted depth coordinate by Rtilt such that
+            Z is gravity direction, Z is up-axis, Y is forward,
+            X is right-ward.
+        upright camera coordinate:
+            Just change axis order and flip up-down axis from upright.
+                depth coordinate
+        image coordinate:
+            ----> x-axis (u)
+           |
+           v
+            y-axis (v)
+        depth points are stored in upright depth coordinate.
+        labels for 3d box (basis, centroid, size) are in upright
+            depth coordinate.
+        2d boxes are in image coordinate
+        We generate frustum point cloud and 3d box
+        in upright camera coordinate.
+    Args:
+        calib_filepath(str): Path of the calib file.
+    """
+    def __init__(self, calib_filepath):
+        lines = [line.rstrip() for line in open(calib_filepath)]
+        Rtilt = np.array([float(x) for x in lines[0].split(' ')])
+        self.Rtilt = np.reshape(Rtilt, (3, 3), order='F')
+        K = np.array([float(x) for x in lines[1].split(' ')])
+        self.K = np.reshape(K, (3, 3), order='F')
+        self.f_u = self.K[0, 0]
+        self.f_v = self.K[1, 1]
+        self.c_u = self.K[0, 2]
+        self.c_v = self.K[1, 2]
+    def project_upright_depth_to_camera(self, pc):
+        """Convert pc coordinate from depth to image.
+        Args:
+            pc(ndarray): Point cloud in depth coordinate.
+        Returns:
+            pc(ndarray): Point cloud in camera coordinate.
+        """
+        # Project upright depth to depth coordinate
+        pc2 = np.dot(np.transpose(self.Rtilt), np.transpose(pc[:,
+                                                               0:3]))  # (3,n)
+        return flip_axis_to_camera(np.transpose(pc2))
+    def project_upright_depth_to_image(self, pc):
+        """Convert pc coordinate from depth to image.
+        Args:
+            pc(ndarray): Point cloud in depth coordinate.
+        Returns:
+            ndarray: [N, 2] uv.
+            ndarray: [n,] depth.
+        """
+        pc2 = self.project_upright_depth_to_camera(pc)
+        uv = np.dot(pc2, np.transpose(self.K))  # (n,3)
+        uv[:, 0] /= uv[:, 2]
+        uv[:, 1] /= uv[:, 2]
+        return uv[:, 0:2], pc2[:, 2]
+    def project_image_to_camera(self, uv_depth):
+        n = uv_depth.shape[0]
+        x = ((uv_depth[:, 0] - self.c_u) * uv_depth[:, 2]) / self.f_u
+        y = ((uv_depth[:, 1] - self.c_v) * uv_depth[:, 2]) / self.f_v
+        pts_3d_camera = np.zeros((n, 3))
+        pts_3d_camera[:, 0] = x
+        pts_3d_camera[:, 1] = y
+        pts_3d_camera[:, 2] = uv_depth[:, 2]
+        return pts_3d_camera
+def rotz(t):
+    """Rotation about the z-axis.
+    Args:
+        t(float): Heading angle.
+    Returns:
+        ndarray: Transforation matrix
+    """
+    c = np.cos(t)
+    s = np.sin(t)
+    return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
+def transform_from_rot_trans(R, t):
+    """Transforation matrix from rotation matrix and translation vector.
+    Args:
+        R(ndarray): Rotation matrix.
+        t(ndarray): Translation vector.
+    Returns:
+        ndarray: Transforation matrix.
+    """
+    R = R.reshape(3, 3)
+    t = t.reshape(3, 1)
+    return np.vstack((np.hstack([R, t]), [0, 0, 0, 1]))
+def read_sunrgbd_label(label_filename):
+    lines = [line.rstrip() for line in open(label_filename)]
+    objects = [SUNObject3d(line) for line in lines]
+    return objects
+def load_image(img_filename):
+    return cv2.imread(img_filename)
+def load_depth_points(depth_filename):
+    depth = np.loadtxt(depth_filename)
+    return depth
+def load_depth_points_mat(depth_filename):
+    depth = sio.loadmat(depth_filename)['instance']
+    return depth
+def in_hull(p, hull):
+    from scipy.spatial import Delaunay
+    if not isinstance(hull, Delaunay):
+        hull = Delaunay(hull)
+    return hull.find_simplex(p) >= 0
+def extract_pc_in_box3d(pc, box3d):
+    """Extract point cloud in box3d.
+    Args:
+        pc(ndarray): [N, 3] Point cloud.
+        box3d(ndarray): [8,3] 3d box.
+    Returns:
+        ndarray: Selected point cloud.
+        ndarray: Indices of selected point cloud.
+    """
+    box3d_roi_inds = in_hull(pc[:, 0:3], box3d)
+    return pc[box3d_roi_inds, :], box3d_roi_inds
+def my_compute_box_3d(center, size, heading_angle):
+    R = rotz(-1 * heading_angle)
+    l, w, h = size
+    x_corners = [-l, l, l, -l, -l, l, l, -l]
+    y_corners = [w, w, -w, -w, w, w, -w, -w]
+    z_corners = [h, h, h, h, -h, -h, -h, -h]
+    corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
+    corners_3d[0, :] += center[0]
+    corners_3d[1, :] += center[1]
+    corners_3d[2, :] += center[2]
+    return np.transpose(corners_3d)
+def compute_box_3d(obj, calib):
+    """Takes an object and a projection matrix (P) and projects the 3d
+        bounding box into the image plane.
+    Args:
+        obj(SUNObject3d): Instance of SUNObject3d.
+        calib(SUNRGBD_Calibration): Instance of SUNRGBD_Calibration.
+    Returns:
+        ndarray: [8,2] array in image coord.
+        corners_3d: [8,3] array in in upright depth coord.
+    """
+    center = obj.centroid
+    # compute rotational matrix around yaw axis
+    R = rotz(-1 * obj.heading_angle)
+    # 3d bounding box dimensions
+    length = obj.length  # along heading arrow
+    width = obj.width  # perpendicular to heading arrow
+    height = obj.height
+    # rotate and translate 3d bounding box
+    x_corners = [
+        -length, length, length, -length, -length, length, length, -length
+    ]
+    y_corners = [width, width, -width, -width, width, width, -width, -width]
+    z_corners = [
+        height, height, height, height, -height, -height, -height, -height
+    ]
+    corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
+    corners_3d[0, :] += center[0]
+    corners_3d[1, :] += center[1]
+    corners_3d[2, :] += center[2]
+    # project the 3d bounding box into the image plane
+    corners_2d, _ = calib.project_upright_depth_to_image(
+        np.transpose(corners_3d))
+    return corners_2d, np.transpose(corners_3d)