Merge rot scale

configs/_base_/datasets/kitti-3d-3class.py

@@ -8,7 +8,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
         filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
@@ -40,7 +39,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.5],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/_base_/datasets/kitti-3d-car.py

@@ -8,7 +8,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
     classes=class_names,
     sample_groups=dict(Car=15))
@@ -38,7 +37,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.5],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/_base_/datasets/nus-3d.py

@@ -42,7 +42,7 @@ train_pipeline = [
         rot_range=[-0.3925, 0.3925],
         scale_ratio_range=[0.95, 1.05],
         translation_std=[0, 0, 0]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),

configs/_base_/datasets/scannet-3d-18class.py

@@ -22,12 +22,16 @@ train_pipeline = [
         valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
                        36, 39)),
     dict(type='IndoorPointSample', num_points=40000),
-    dict(type='IndoorFlipData', flip_ratio_yz=0.5, flip_ratio_xz=0.5),
     dict(
-        type='IndoorGlobalRotScaleTrans',
-        shift_height=True,
-        rot_range=[-1 / 36, 1 / 36],
-        scale_range=None),
+        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',
@@ -53,7 +57,11 @@ test_pipeline = [
                 rot_range=[0, 0],
                 scale_ratio_range=[1., 1.],
                 translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.5,
+                flip_ratio_bev_vertical=0.5),
             dict(type='IndoorPointSample', num_points=40000),
             dict(
                 type='DefaultFormatBundle3D',

configs/_base_/datasets/sunrgbd-3d-10class.py

@@ -9,12 +9,16 @@ train_pipeline = [
         load_dim=6,
         use_dim=[0, 1, 2]),
     dict(type='LoadAnnotations3D'),
-    dict(type='IndoorFlipData', flip_ratio_yz=0.5),
     dict(
-        type='IndoorGlobalRotScaleTrans',
-        shift_height=True,
-        rot_range=[-1 / 6, 1 / 6],
-        scale_range=[0.85, 1.15]),
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+    ),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.523599, 0.523599],
+        scale_ratio_range=[0.85, 1.15],
+        shift_height=True),
     dict(type='IndoorPointSample', num_points=20000),
     dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
@@ -36,7 +40,11 @@ test_pipeline = [
                 rot_range=[0, 0],
                 scale_ratio_range=[1., 1.],
                 translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.5,
+            ),
             dict(type='IndoorPointSample', num_points=20000),
             dict(
                 type='DefaultFormatBundle3D',

configs/_base_/schedules/schedule_3x.py

@@ -2,7 +2,7 @@
 # This schedule is mainly used by models on indoor dataset,
 # e.g., VoteNet on SUNRGBD and ScanNet
 lr = 0.008  # max learning rate
-optimizer = dict(type='Adam', lr=lr)
+optimizer = dict(type='AdamW', lr=lr, weight_decay=0.01)
 optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
 lr_config = dict(policy='step', warmup=None, step=[24, 32])
 # runtime settings

configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py

@@ -81,7 +81,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
     sample_groups=dict(Car=15),
     classes=class_names)
@@ -96,7 +95,7 @@ train_pipeline = [
         loc_noise_std=[0.25, 0.25, 0.25],
         global_rot_range=[0.0, 0.0],
         rot_uniform_noise=[-0.15707963267, 0.15707963267]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScale',
         rot_uniform_noise=[-0.78539816, 0.78539816],

configs/benchmark/hv_pointpillars_secfpn_6x8_160e_pcdet_kitti-3d-3class.py

@@ -110,7 +110,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
         filter_by_min_points=dict(
@@ -135,7 +134,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.1],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/benchmark/hv_second_secfpn_6x8_80e_pcdet_kitti-3d-3class.py

@@ -99,7 +99,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
         filter_by_min_points=dict(
@@ -135,7 +134,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.1],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py

@@ -148,7 +148,7 @@ train_pipeline = [
         rot_range=[-0.78539816, 0.78539816],
         scale_ratio_range=[0.95, 1.05],
         translation_std=[0.2, 0.2, 0.2]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py

@@ -204,7 +204,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
         filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
@@ -220,7 +219,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.5],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py

@@ -78,7 +78,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
     classes=class_names,
     sample_groups=dict(Car=15))
@@ -92,7 +91,7 @@ train_pipeline = [
         translation_std=[1.0, 1.0, 0.5],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

@@ -13,7 +13,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
         filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
@@ -31,7 +30,7 @@ train_pipeline = [
         translation_std=[0.25, 0.25, 0.25],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.15707963267, 0.15707963267]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -35,7 +35,6 @@ db_sampler = dict(
     data_root=data_root,
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
-    object_rot_range=[0.0, 0.0],
     prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
     sample_groups=dict(Car=15),
     classes=class_names)
@@ -50,7 +49,7 @@ train_pipeline = [
         translation_std=[0.25, 0.25, 0.25],
         global_rot_range=[0.0, 0.0],
         rot_range=[-0.15707963267, 0.15707963267]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
     dict(
         type='GlobalRotScaleTrans',
         rot_range=[-0.78539816, 0.78539816],

mmdet3d/core/bbox/structures/cam_box3d.py

@@ -101,6 +101,9 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
         Returns:
             torch.Tensor: corners of each box with size (N, 8, 3)
         """
+        # TODO: rotation_3d_in_axis function do not support
+        #  empty tensor currently.
+        assert len(self.tensor) != 0
         dims = self.dims
         corners_norm = torch.from_numpy(
             np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)).to(
@@ -150,11 +153,18 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
         bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
         return bev_boxes
 
-    def rotate(self, angle):
-        """Calculate whether the points is in any of the boxes
+    def rotate(self, angle, points=None):
+        """Rotate boxes with points (optional) with the given angle.
 
         Args:
-            angle (float | torch.Tensor): rotation angle
+            angle (float, torch.Tensor): Rotation angle.
+            points (torch.Tensor, numpy.ndarray, optional): Points to rotate.
+                Defaults to None.
+
+        Returns:
+            tuple or None: When ``points`` is None, the function returns None,
+                otherwise it returns the rotated points and the
+                rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
             angle = self.tensor.new_tensor(angle)
@@ -166,13 +176,28 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
         self.tensor[:, :3] = self.tensor[:, :3] @ rot_mat_T
         self.tensor[:, 6] += angle
 
-    def flip(self, bev_direction='horizontal'):
+        if points is not None:
+            if isinstance(points, torch.Tensor):
+                points[:, :3] = points[:, :3] @ rot_mat_T
+            elif isinstance(points, np.ndarray):
+                rot_mat_T = rot_mat_T.numpy()
+                points[:, :3] = np.dot(points[:, :3], rot_mat_T)
+            else:
+                raise ValueError
+            return points, rot_mat_T
+
+    def flip(self, bev_direction='horizontal', points=None):
         """Flip the boxes in BEV along given BEV direction
 
         In CAM coordinates, it flips the x (horizontal) or z (vertical) axis.
 
         Args:
             bev_direction (str): Flip direction (horizontal or vertical).
+            points (torch.Tensor, numpy.ndarray, None): Points to flip.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor, numpy.ndarray or None: Flipped points.
         """
         assert bev_direction in ('horizontal', 'vertical')
         if bev_direction == 'horizontal':
@@ -184,6 +209,14 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
             if self.with_yaw:
                 self.tensor[:, 6] = -self.tensor[:, 6]
 
+        if points is not None:
+            assert isinstance(points, (torch.Tensor, np.ndarray))
+            if bev_direction == 'horizontal':
+                points[:, 0] = -points[:, 0]
+            elif bev_direction == 'vertical':
+                points[:, 2] = -points[:, 2]
+            return points
+
     def in_range_bev(self, box_range):
         """Check whether the boxes are in the given range
 

mmdet3d/core/bbox/structures/depth_box3d.py

@@ -69,6 +69,9 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
         Returns:
             torch.Tensor: corners of each box with size (N, 8, 3)
         """
+        # TODO: rotation_3d_in_axis function do not support
+        #  empty tensor currently.
+        assert len(self.tensor) != 0
         dims = self.dims
         corners_norm = torch.from_numpy(
             np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)).to(
@@ -118,23 +121,31 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
         bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
         return bev_boxes
 
-    def rotate(self, angle):
-        """Calculate whether the points is in any of the boxes
+    def rotate(self, angle, points=None):
+        """Rotate boxes with points (optional) with the given angle.
 
         Args:
-            angle (float | torch.Tensor): rotation angle
+            angle (float, torch.Tensor): Rotation angle.
+            points (torch.Tensor, numpy.ndarray, optional): Points to rotate.
+                Defaults to None.
+
+        Returns:
+            tuple or None: When ``points`` is None, the function returns None,
+                otherwise it returns the rotated points and the
+                rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
             angle = self.tensor.new_tensor(angle)
         rot_sin = torch.sin(angle)
         rot_cos = torch.cos(angle)
-        rot_mat = self.tensor.new_tensor([[rot_cos, -rot_sin, 0],
-                                          [rot_sin, rot_cos, 0], [0, 0, 1]])
-        self.tensor[:, 0:3] = self.tensor[:, 0:3] @ rot_mat.T
+        rot_mat_T = self.tensor.new_tensor([[rot_cos, -rot_sin, 0],
+                                            [rot_sin, rot_cos, 0], [0, 0,
+                                                                    1]]).T
+        self.tensor[:, 0:3] = self.tensor[:, 0:3] @ rot_mat_T
         if self.with_yaw:
             self.tensor[:, 6] -= angle
         else:
-            corners_rot = self.corners @ rot_mat.T
+            corners_rot = self.corners @ rot_mat_T
             new_x_size = corners_rot[..., 0].max(
                 dim=1, keepdim=True)[0] - corners_rot[..., 0].min(
                     dim=1, keepdim=True)[0]
@@ -143,13 +154,28 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
                     dim=1, keepdim=True)[0]
             self.tensor[:, 3:5] = torch.cat((new_x_size, new_y_size), dim=-1)
 
-    def flip(self, bev_direction='horizontal'):
+        if points is not None:
+            if isinstance(points, torch.Tensor):
+                points[:, :3] = points[:, :3] @ rot_mat_T
+            elif isinstance(points, np.ndarray):
+                rot_mat_T = rot_mat_T.numpy()
+                points[:, :3] = np.dot(points[:, :3], rot_mat_T)
+            else:
+                raise ValueError
+            return points, rot_mat_T
+
+    def flip(self, bev_direction='horizontal', points=None):
         """Flip the boxes in BEV along given BEV direction
 
         In Depth coordinates, it flips x (horizontal) or y (vertical) axis.
 
         Args:
             bev_direction (str): Flip direction (horizontal or vertical).
+            points (torch.Tensor, numpy.ndarray, None): Points to flip.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor, numpy.ndarray or None: Flipped points.
         """
         assert bev_direction in ('horizontal', 'vertical')
         if bev_direction == 'horizontal':
@@ -161,6 +187,14 @@ class DepthInstance3DBoxes(BaseInstance3DBoxes):
             if self.with_yaw:
                 self.tensor[:, 6] = -self.tensor[:, 6]
 
+        if points is not None:
+            assert isinstance(points, (torch.Tensor, np.ndarray))
+            if bev_direction == 'horizontal':
+                points[:, 0] = -points[:, 0]
+            elif bev_direction == 'vertical':
+                points[:, 1] = -points[:, 1]
+            return points
+
     def in_range_bev(self, box_range):
         """Check whether the boxes are in the given range
 

mmdet3d/core/bbox/structures/lidar_box3d.py

@@ -69,6 +69,9 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
         Returns:
             torch.Tensor: corners of each box with size (N, 8, 3)
         """
+        # TODO: rotation_3d_in_axis function do not support
+        #  empty tensor currently.
+        assert len(self.tensor) != 0
         dims = self.dims
         corners_norm = torch.from_numpy(
             np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)).to(
@@ -118,11 +121,18 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
         bev_boxes = torch.cat([centers - dims / 2, centers + dims / 2], dim=-1)
         return bev_boxes
 
-    def rotate(self, angle):
-        """Calculate whether the points is in any of the boxes
+    def rotate(self, angle, points=None):
+        """Rotate boxes with points (optional) with the given angle.
 
         Args:
-            angle (float | torch.Tensor): rotation angle
+            angle (float | torch.Tensor): Rotation angle.
+            points (torch.Tensor, numpy.ndarray, optional): Points to rotate.
+                Defaults to None.
+
+        Returns:
+            tuple or None: When ``points`` is None, the function returns None,
+                otherwise it returns the rotated points and the
+                rotation matrix ``rot_mat_T``.
         """
         if not isinstance(angle, torch.Tensor):
             angle = self.tensor.new_tensor(angle)
@@ -138,13 +148,28 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             # rotate velo vector
             self.tensor[:, 7:9] = self.tensor[:, 7:9] @ rot_mat_T[:2, :2]
 
-    def flip(self, bev_direction='horizontal'):
+        if points is not None:
+            if isinstance(points, torch.Tensor):
+                points[:, :3] = points[:, :3] @ rot_mat_T
+            elif isinstance(points, np.ndarray):
+                rot_mat_T = rot_mat_T.numpy()
+                points[:, :3] = np.dot(points[:, :3], rot_mat_T)
+            else:
+                raise ValueError
+            return points, rot_mat_T
+
+    def flip(self, bev_direction='horizontal', points=None):
         """Flip the boxes in BEV along given BEV direction
 
         In LIDAR coordinates, it flips the y (horizontal) or x (vertical) axis.
 
         Args:
             bev_direction (str): Flip direction (horizontal or vertical).
+            points (torch.Tensor, numpy.ndarray, None): Points to flip.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor, numpy.ndarray or None: Flipped points.
         """
         assert bev_direction in ('horizontal', 'vertical')
         if bev_direction == 'horizontal':
@@ -156,6 +181,14 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
             if self.with_yaw:
                 self.tensor[:, 6] = -self.tensor[:, 6]
 
+        if points is not None:
+            assert isinstance(points, (torch.Tensor, np.ndarray))
+            if bev_direction == 'horizontal':
+                points[:, 1] = -points[:, 1]
+            elif bev_direction == 'vertical':
+                points[:, 0] = -points[:, 0]
+            return points
+
     def in_range_bev(self, box_range):
         """Check whether the boxes are in the given range
 

mmdet3d/core/bbox/transforms.py

 import torch
 
 
-def bbox3d_mapping_back(bboxes, scale_factor, flip):
+def bbox3d_mapping_back(bboxes, scale_factor, flip_horizontal, flip_vertical):
     """Map bboxes from testing scale to original image scale"""
     new_bboxes = bboxes.clone()
-    if flip:
-        new_bboxes.flip()
+    if flip_horizontal:
+        new_bboxes.flip('horizontal')
+    if flip_vertical:
+        new_bboxes.flip('vertical')
     new_bboxes.scale(1 / scale_factor)
 
     return new_bboxes

mmdet3d/core/post_processing/merge_augs.py

@@ -33,10 +33,12 @@ def merge_aug_bboxes_3d(aug_results, img_metas, test_cfg):
 
     for bboxes, img_info in zip(aug_results, img_metas):
         scale_factor = img_info[0]['pcd_scale_factor']
-        flip = img_info[0]['pcd_flip']
+        pcd_horizontal_flip = img_info[0]['pcd_horizontal_flip']
+        pcd_vertical_flip = img_info[0]['pcd_vertical_flip']
         recovered_scores.append(bboxes['scores_3d'])
         recovered_labels.append(bboxes['labels_3d'])
-        bboxes = bbox3d_mapping_back(bboxes['boxes_3d'], scale_factor, flip)
+        bboxes = bbox3d_mapping_back(bboxes['boxes_3d'], scale_factor,
+                                     pcd_horizontal_flip, pcd_vertical_flip)
         recovered_bboxes.append(bboxes)
 
     aug_bboxes = recovered_bboxes[0].cat(recovered_bboxes)

mmdet3d/datasets/__init__.py

@@ -3,12 +3,11 @@ from .custom_3d import Custom3DDataset
 from .kitti2d_dataset import Kitti2DDataset
 from .kitti_dataset import KittiDataset
 from .nuscenes_dataset import NuScenesDataset
-from .pipelines import (GlobalRotScaleTrans, IndoorFlipData,
-                        IndoorGlobalRotScaleTrans, IndoorPointSample,
-                        IndoorPointsColorJitter, LoadAnnotations3D,
-                        LoadPointsFromFile, NormalizePointsColor, ObjectNoise,
-                        ObjectRangeFilter, ObjectSample, PointShuffle,
-                        PointsRangeFilter, RandomFlip3D)
+from .pipelines import (GlobalRotScaleTrans, IndoorPointSample,
+                        LoadAnnotations3D, LoadPointsFromFile,
+                        NormalizePointsColor, ObjectNoise, ObjectRangeFilter,
+                        ObjectSample, PointShuffle, PointsRangeFilter,
+                        RandomFlip3D)
 from .scannet_dataset import ScanNetDataset
 from .sunrgbd_dataset import SUNRGBDDataset
 
@@ -20,7 +19,5 @@ __all__ = [
     'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans', 'PointShuffle',
     'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
     'LoadPointsFromFile', 'NormalizePointsColor', 'IndoorPointSample',
-    'LoadAnnotations3D', 'IndoorPointsColorJitter',
-    'IndoorGlobalRotScaleTrans', 'IndoorFlipData', 'SUNRGBDDataset',
-    'ScanNetDataset', 'Custom3DDataset'
+    'LoadAnnotations3D', 'SUNRGBDDataset', 'ScanNetDataset', 'Custom3DDataset'
 ]