# ScanNet for 3D Object Detection ## Dataset preparation For the overall process, please refer to the [README](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md/) page for ScanNet. ### Export ScanNet point cloud data By exporting ScanNet point cloud data, we load the raw point cloud data and generate the relevant annotations including semantic label, instance label and ground truth bounding boxes. ```shell python batch_load_scannet_data.py ``` The directory structure before data preparation should be as below ``` mmdetection3d ├── mmdet3d ├── tools ├── configs ├── data │ ├── scannet │ │ ├── meta_data │ │ ├── scans │ │ │ ├── scenexxxx_xx │ │ ├── batch_load_scannet_data.py │ │ ├── load_scannet_data.py │ │ ├── scannet_utils.py │ │ ├── README.md ``` Under folder `scans` there are overall 1201 train and 312 validation folders in which raw point cloud data and relevant annotations are saved. For instance, under folder `scene0001_01` the files are as below: - `scene0001_01_vh_clean_2.ply`: Mesh file including raw point cloud data. - `scene0001_01.aggregation.json`: Aggregation file including object id, segments id and label. - `scene0001_01_vh_clean_2.0.010000.segs.json`: Segmentation file including segments id and vertex. - `scene0001_01.txt`: Meta file including axis-aligned matrix, etc. - `scene0001_01_vh_clean_2.labels.ply` Export ScanNet data by running `python batch_load_scannet_data.py`. The main steps include: - Export original files to point cloud, instance label, semantic label and bounding box file. - Downsample raw point cloud and filter invalid classes. - Save point cloud data and relevant annotation files. And the core function `export` in `load_scannet_data.py` is as follows: ```python def export(mesh_file, agg_file, seg_file, meta_file, label_map_file, output_file=None, test_mode=False): # label map file: ./data/scannet/meta_data/scannetv2-labels.combined.tsv # the various label standards in the label map file, e.g. 'nyu40id' label_map = scannet_utils.read_label_mapping( label_map_file, label_from='raw_category', label_to='nyu40id') # load raw point cloud data, 6-dims feature: XYZRGB mesh_vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file) # Load scene axis alignment matrix: a 4x4 transformation matrix # transform raw points in sensor coordinate system to a coordinate system # which is axis-aligned with the length/width of the room lines = open(meta_file).readlines() # test set data doesn't have align_matrix axis_align_matrix = np.eye(4) 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)) # perform global alignment of mesh vertices pts = np.ones((mesh_vertices.shape[0], 4)) # raw point cloud in homogeneous coordinats, each row: [x, y, z, 1] pts[:, 0:3] = mesh_vertices[:, 0:3] # transform raw mesh vertices to aligned mesh vertices pts = np.dot(pts, axis_align_matrix.transpose()) # Nx4 aligned_mesh_vertices = np.concatenate([pts[:, 0:3], mesh_vertices[:, 3:]], axis=1) # Load semantic and instance labels if not test_mode: # each object has one semantic label and consists of several segments object_id_to_segs, label_to_segs = read_aggregation(agg_file) # many points may belong to the same segment 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] # each point has one semantic label label_ids[verts] = label_id instance_ids = np.zeros( shape=(num_verts), dtype=np.uint32) # 0: unannotated for object_id, segs in object_id_to_segs.items(): for seg in segs: verts = seg_to_verts[seg] # object_id is 1-indexed, i.e. 1,2,3,.,,,.NUM_INSTANCES # each point belongs to one object 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] # bbox format is [x, y, z, dx, dy, dz, label_id] # [x, y, z] is gravity center of bbox, [dx, dy, dz] is axis-aligned # [label_id] is semantic label id in 'nyu40id' standard # Note: since 3d bbox is axis-aligned, the yaw is 0. unaligned_bboxes = extract_bbox(mesh_vertices, object_id_to_segs, object_id_to_label_id, instance_ids) aligned_bboxes = extract_bbox(aligned_mesh_vertices, object_id_to_segs, object_id_to_label_id, instance_ids) ... return mesh_vertices, label_ids, instance_ids, unaligned_bboxes, \ aligned_bboxes, object_id_to_label_id, axis_align_matrix ``` After exporting each scan, the raw point cloud could be downsampled, e.g. to 50000, if the number of points is too large. In addition, invalid semantic labels outside of `nyu40id` standard or optional `DONOT CARE` classes should be filtered. Finally, the point cloud data, semantic labels, instance labels and ground truth bounding boxes should be saved in `.npy` files. ### Export ScanNet RGB data By exporting ScanNet RGB data, for each scene we load a set of RGB images with corresponding 4x4 pose matrices, and a single 4x4 camera intrinsic matrix. Note, that this step is optional and can be skipped if multi-view detection is not planned to use. ```shell python extract_posed_images.py ``` Each of 1201 train, 312 validation and 100 test scenes contains a single `.sens` file. For instance, for scene `0001_01` we have `data/scannet/scans/scene0001_01/0001_01.sens`. For this scene all images and poses are extracted to `data/scannet/posed_images/scene0001_01`. Specifically, there will be 300 image files xxxxx.jpg, 300 camera pose files xxxxx.txt and a single `intrinsic.txt` file. Typically, single scene contains several thousand images. By default, we extract only 300 of them with resulting weight of <100 Gb. To extract more images, use `--max-images-per-scene` parameter. ### Create dataset ```shell python tools/create_data.py scannet --root-path ./data/scannet \ --out-dir ./data/scannet --extra-tag scannet ``` The above exported point cloud file, semantic label file and instance label file are further saved in `.bin` format. Meanwhile `.pkl` info files are also generated for train or validation. The core function `process_single_scene` of getting data infos is as follows. ```python def process_single_scene(sample_idx): # save point cloud, instance label and semantic label in .bin file respectively, get info['pts_path'], info['pts_instance_mask_path'] and info['pts_semantic_mask_path'] ... # get annotations if has_label: annotations = {} # box is of shape [k, 6 + class] aligned_box_label = self.get_aligned_box_label(sample_idx) unaligned_box_label = self.get_unaligned_box_label(sample_idx) annotations['gt_num'] = aligned_box_label.shape[0] if annotations['gt_num'] != 0: aligned_box = aligned_box_label[:, :-1] # k, 6 unaligned_box = unaligned_box_label[:, :-1] classes = aligned_box_label[:, -1] # k annotations['name'] = np.array([ self.label2cat[self.cat_ids2class[classes[i]]] for i in range(annotations['gt_num']) ]) # default names are given to aligned bbox for compatibility # we also save unaligned bbox info with marked names annotations['location'] = aligned_box[:, :3] annotations['dimensions'] = aligned_box[:, 3:6] annotations['gt_boxes_upright_depth'] = aligned_box annotations['unaligned_location'] = unaligned_box[:, :3] annotations['unaligned_dimensions'] = unaligned_box[:, 3:6] annotations[ 'unaligned_gt_boxes_upright_depth'] = unaligned_box annotations['index'] = np.arange( annotations['gt_num'], dtype=np.int32) annotations['class'] = np.array([ self.cat_ids2class[classes[i]] for i in range(annotations['gt_num']) ]) axis_align_matrix = self.get_axis_align_matrix(sample_idx) annotations['axis_align_matrix'] = axis_align_matrix # 4x4 info['annos'] = annotations return info ``` The directory structure after process should be as below ``` scannet ├── scannet_utils.py ├── batch_load_scannet_data.py ├── load_scannet_data.py ├── scannet_utils.py ├── README.md ├── scans ├── scans_test ├── scannet_instance_data ├── points │ ├── xxxxx.bin ├── instance_mask │ ├── xxxxx.bin ├── semantic_mask │ ├── xxxxx.bin ├── seg_info │ ├── train_label_weight.npy │ ├── train_resampled_scene_idxs.npy │ ├── val_label_weight.npy │ ├── val_resampled_scene_idxs.npy ├── posed_images │ ├── scenexxxx_xx │ │ ├── xxxxxx.txt │ │ ├── xxxxxx.jpg │ │ ├── intrinsic.txt ├── scannet_infos_train.pkl ├── scannet_infos_val.pkl ├── scannet_infos_test.pkl ``` - `points/xxxxx.bin`: The `axis-unaligned` point cloud data after downsample. Note: the point would be axis-aligned in pre-processing `GlobalAlignment` of 3d detection task. - `instance_mask/xxxxx.bin`: The instance label for each point, value range: [0, NUM_INSTANCES], 0: unannotated. - `semantic_mask/xxxxx.bin`: The semantic label for each point, value range: [1, 40], i.e. `nyu40id` standard. Note: the `nyu40id` id will be mapped to train id in train pipeline `PointSegClassMapping`. - `posed_images/scenexxxx_xx`: The set of `.jpg` images with `.txt` 4x4 poses and the single `.txt` file with camera intrinsic matrix. - `scannet_infos_train.pkl`: The train data infos, the detailed info of each scan is as follows: - info['point_cloud']: {'num_features': 6, 'lidar_idx': sample_idx}. - info['pts_path']: The path of `points/xxxxx.bin`. - info['pts_instance_mask_path']: The path of `instance_mask/xxxxx.bin`. - info['pts_semantic_mask_path']: The path of `semantic_mask/xxxxx.bin`. - info['annos']: The annotations of each scan. - annotations['gt_num']: The number of ground truth. - annotations['name']： The semantic name of all ground truths, e.g. `chair`. - annotations['location']: The gravity center of axis-aligned 3d bounding box. Shape: [K, 3], K is the number of ground truth. - annotations['dimensions']: The dimensions of axis-aligned 3d bounding box, i.e. x_size, y_size, z_size, shape: [K, 3]. - annotations['gt_boxes_upright_depth']: Axis-aligned 3d bounding box, each bounding box is x, y, z, x_size, y_size, z_size, shape: [K, 6]. - annotations['unaligned_location']: The gravity center of axis-unaligned 3d bounding box. - annotations['unaligned_dimensions']: The dimensions of axis-unaligned 3d bounding box. - annotations['unaligned_gt_boxes_upright_depth']: Axis-unaligned 3d bounding box. - annotations['index']: The index of all ground truths, i.e. [0, K). - annotations['class']: The train class id of each bounding box, value range: [0, 18), shape: [K, ]. ## Train pipeline A typical train pipeline of ScanNet for 3d detection is as below. ```python train_pipeline = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=True, load_dim=6, use_dim=[0, 1, 2]), dict( type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_mask_3d=True, with_seg_3d=True), dict(type='GlobalAlignment', rotation_axis=2), dict( type='PointSegClassMapping', valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39), max_cat_id=40), dict(type='IndoorPointSample', num_points=40000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.087266, 0.087266], scale_ratio_range=[1.0, 1.0], shift_height=True), dict(type='DefaultFormatBundle3D', class_names=class_names), dict( type='Collect3D', keys=[ 'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask', 'pts_instance_mask' ]) ] ``` - `GlobalAlignment`: The previous point cloud would be axis-aligned using the axis-aligned matrix. - `PointSegClassMapping`: Only the valid category id will be mapped to train class label id like [0, 18). - Data augmentation: - `IndoorPointSample`: downsample input point cloud. - `RandomFlip3D`: randomly flip input point cloud horizontally or vertically. - `GlobalRotScaleTrans`: rotate input point cloud, usually [-5, 5] degree. ## Metrics Typically mean average precision (mAP) is used for evaluation on ScanNet, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic functions to compute precision and recall for 3d object detection for multiple classes is called, please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/indoor_eval.py). As introduced in section `Export ScanNet data`, all ground truth 3d bounding box are axis-aligned, i.e. the yaw is zero. So the yaw target of network predicted 3d bounding box is also zero and axis-aligned 3d non-maximum suppression (NMS) is adopted during post-processing without reagrd to rotation.