[Enhance] Migrate to PointSample and enhance PointSample function

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

@@ -24,7 +24,7 @@
         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='PointSample', num_points=40000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -67,7 +67,7 @@
                 sync_2d=False,
                 flip_ratio_bev_horizontal=0.5,
                 flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=40000),
+            dict(type='PointSample', num_points=40000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=class_names,

configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256.py

@@ -92,7 +92,7 @@
         type='PointSegClassMapping',
         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=50000),
+    dict(type='PointSample', num_points=50000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -133,7 +133,7 @@
                 sync_2d=False,
                 flip_ratio_bev_horizontal=0.5,
                 flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=50000),
+            dict(type='PointSample', num_points=50000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=class_names,

configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256.py

@@ -91,7 +91,7 @@
         type='PointSegClassMapping',
         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=50000),
+    dict(type='PointSample', num_points=50000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -132,7 +132,7 @@
                 sync_2d=False,
                 flip_ratio_bev_horizontal=0.5,
                 flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=50000),
+            dict(type='PointSample', num_points=50000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=class_names,

configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256.py

@@ -107,7 +107,7 @@
         type='PointSegClassMapping',
         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=50000),
+    dict(type='PointSample', num_points=50000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -148,7 +148,7 @@
                 sync_2d=False,
                 flip_ratio_bev_horizontal=0.5,
                 flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=50000),
+            dict(type='PointSample', num_points=50000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=class_names,

configs/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512.py

@@ -108,7 +108,7 @@
         type='PointSegClassMapping',
         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=50000),
+    dict(type='PointSample', num_points=50000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -149,7 +149,7 @@
                 sync_2d=False,
                 flip_ratio_bev_horizontal=0.5,
                 flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=50000),
+            dict(type='PointSample', num_points=50000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=class_names,

docs/compatibility.md

@@ -61,7 +61,7 @@ We adopt new pre-processing and conversion steps of ScanNet dataset. In previous
 
 - Since the aligned boxes share the same key as in old data infos, we do not need to modify the code related to it. But do remember that they are not in the same coordinate system as the saved points.
 
-- There is an `IndoorPointSample` pipeline in the data pipelines for ScanNet detection task which down-samples points. So removing down-sampling in data generation will not affect the code.
+- There is an `PointSample` pipeline in the data pipelines for ScanNet detection task which down-samples points. So removing down-sampling in data generation will not affect the code.
 
 We have trained a [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/votenet_8x8_scannet-3d-18class.py) model on the newly processed ScanNet dataset and get similar benchmark results. In order to prepare ScanNet data for both detection and segmentation tasks, please re-run the new pre-processing scripts following the ScanNet [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/data/scannet/README.md/).
 

docs/datasets/scannet_det.md

@@ -267,7 +267,7 @@ train_pipeline = [
         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='PointSample', num_points=40000),
     dict(
         type='RandomFlip3D',
         sync_2d=False,
@@ -291,7 +291,7 @@ train_pipeline = [
 - `GlobalAlignment`: The previous point cloud would be axis-aligned using the axis-aligned matrix.
 - `PointSegClassMapping`: Only the valid category ids will be mapped to class label ids like [0, 18) during training.
 - Data augmentation:
-    - `IndoorPointSample`: downsample the input point cloud.
+    - `PointSample`: downsample the input point cloud.
     - `RandomFlip3D`: randomly flip the input point cloud horizontally or vertically.
     - `GlobalRotScaleTrans`: rotate the input point cloud, usually in the range of [-5, 5] (degrees) for ScanNet; then scale the input point cloud, usually by 1.0 for ScanNet; finally translate the input point cloud, usually by 0 for ScanNet.
 

docs/datasets/sunrgbd_det.md

@@ -282,7 +282,7 @@ train_pipeline = [
         rot_range=[-0.523599, 0.523599],
         scale_ratio_range=[0.85, 1.15],
         shift_height=True),
-    dict(type='IndoorPointSample', num_points=20000),
+    dict(type='PointSample', num_points=20000),
     dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
@@ -291,7 +291,7 @@ train_pipeline = [
 Data augmentation for point clouds:
 - `RandomFlip3D`: randomly flip the input point cloud horizontally or vertically.
 - `GlobalRotScaleTrans`: rotate the input point cloud, usually in the range of [-30, 30] (degrees) for SUN RGB-D; then scale the input point cloud, usually in the range of [0.85, 1.15] for SUN RGB-D; finally translate the input point cloud, usually by 0 for SUN RGB-D.
-- `IndoorPointSample`: downsample the input point cloud.
+- `PointSample`: downsample the input point cloud.
 
 A typical train pipeline of SUN RGB-D for multi-modality (point cloud and image) 3D detection is as follows.
 
@@ -320,7 +320,7 @@ train_pipeline = [
         rot_range=[-0.523599, 0.523599],
         scale_ratio_range=[0.85, 1.15],
         shift_height=True),
-    dict(type='IndoorPointSample', num_points=20000),
+    dict(type='PointSample', num_points=20000),
     dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(
         type='Collect3D',

docs_zh-CN/datasets/sunrgbd_det.md

@@ -281,7 +281,7 @@ train_pipeline = [
         rot_range=[-0.523599, 0.523599],
         scale_ratio_range=[0.85, 1.15],
         shift_height=True),
-    dict(type='IndoorPointSample', num_points=20000),
+    dict(type='PointSample', num_points=20000),
     dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
@@ -290,7 +290,7 @@ train_pipeline = [
 点云上的数据增强
 - `RandomFlip3D`：随机左右或前后翻转输入点云。
 - `GlobalRotScaleTrans`：旋转输入点云，对于 SUN RGB-D 角度通常落入 [-30, 30] （度）的范围；并放缩输入点云，对于 SUN RGB-D 比例通常落入 [0.85, 1.15] 的范围；最后平移输入点云，对于 SUN RGB-D 通常位移量为 0。
-- `IndoorPointSample`：降采样输入点云。
+- `PointSample`：降采样输入点云。
 
 SUN RGB-D 上多模态（点云和图像）3D 物体检测的经典流程如下：
 
@@ -319,7 +319,7 @@ train_pipeline = [
         rot_range=[-0.523599, 0.523599],
         scale_ratio_range=[0.85, 1.15],
         shift_height=True),
-    dict(type='IndoorPointSample', num_points=20000),
+    dict(type='PointSample', num_points=20000),
     dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(
         type='Collect3D',

docs_zh-CN/tutorials/config.md

@@ -204,7 +204,7 @@ train_pipeline = [  # 训练流水线，更多细节请参考 mmdet3d.datasets.p
         valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
                        36, 39),  # 所有有效类别的编号
         max_cat_id=40),  # 输入语义分割掩码中可能存在的最大类别编号
-    dict(type='IndoorPointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
+    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
             num_points=40000),  # 采样的点的数量
     dict(type='IndoorFlipData',  # 数据增广流程，随机翻转点和 3D 框
         flip_ratio_yz=0.5,  # 沿着 yz 平面被翻转的概率
@@ -233,7 +233,7 @@ test_pipeline = [  # 测试流水线，更多细节请参考 mmdet3d.datasets.pi
         shift_height=True,  # 是否使用变换高度
         load_dim=6,  # 读取的点的维度
         use_dim=[0, 1, 2]),  # 使用所读取点的哪些维度
-    dict(type='IndoorPointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
+    dict(type='PointSample',  # 室内点采样，更多细节请参考 mmdet3d.datasets.pipelines.indoor_sample
             num_points=40000),  # 采样的点的数量
     dict(
         type='DefaultFormatBundle3D',  # 默认格式打包以收集读取的所有数据，更多细节请参考 mmdet3d.datasets.pipelines.formating
@@ -287,7 +287,7 @@ data = dict(
                     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='PointSample', num_points=40000),
                 dict(
                     type='IndoorFlipData',
                     flip_ratio_yz=0.5,
@@ -326,7 +326,7 @@ data = dict(
                 shift_height=True,
                 load_dim=6,
                 use_dim=[0, 1, 2]),
-            dict(type='IndoorPointSample', num_points=40000),
+            dict(type='PointSample', num_points=40000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',
@@ -351,7 +351,7 @@ data = dict(
                 shift_height=True,
                 load_dim=6,
                 use_dim=[0, 1, 2]),
-            dict(type='IndoorPointSample', num_points=40000),
+            dict(type='PointSample', num_points=40000),
             dict(
                 type='DefaultFormatBundle3D',
                 class_names=('cabinet', 'bed', 'chair', 'sofa', 'table',

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -846,6 +846,10 @@ class PointSample(object):
     Args:
         num_points (int): Number of points to be sampled.
         sample_range (float, optional): The range where to sample points.
+            If not None, the points with depth larger than `sample_range` are
+            prior to be sampled. Defaults to None.
+        replace (bool, optional): Whether the sampling is with or without
+            replacement. Defaults to False.
     """
 
     def __init__(self, num_points, sample_range=None, replace=False):
@@ -867,8 +871,7 @@ def _points_random_sampling(self,
             points (np.ndarray | :obj:`BasePoints`): 3D Points.
             num_samples (int): Number of samples to be sampled.
             sample_range (float, optional): Indicating the range where the
-                points will be sampled.
-                Defaults to None.
+                points will be sampled. Defaults to None.
             replace (bool, optional): Sampling with or without replacement.
                 Defaults to None.
             return_choices (bool, optional): Whether return choice.
@@ -886,6 +889,10 @@ def _points_random_sampling(self,
             depth = np.linalg.norm(points.tensor, axis=1)
             far_inds = np.where(depth > sample_range)[0]
             near_inds = np.where(depth <= sample_range)[0]
+            # in case there are too many far points
+            if len(far_inds) > num_samples:
+                far_inds = np.random.choice(
+                    far_inds, num_samples, replace=False)
             point_range = near_inds
             num_samples -= len(far_inds)
         choices = np.random.choice(point_range, num_samples, replace=replace)
@@ -907,11 +914,11 @@ def __call__(self, results):
             dict: Results after sampling, 'points', 'pts_instance_mask' \
                 and 'pts_semantic_mask' keys are updated in the result dict.
         """
-        from mmdet3d.core.points import CameraPoints
         points = results['points']
         # Points in Camera coord can provide the depth information.
         # TODO: Need to suport distance-based sampling for other coord system.
         if self.sample_range is not None:
+            from mmdet3d.core.points import CameraPoints
             assert isinstance(points, CameraPoints), \
                 'Sampling based on distance is only appliable for CAMERA coord'
         points, choices = self._points_random_sampling(
@@ -939,7 +946,8 @@ def __repr__(self):
         """str: Return a string that describes the module."""
         repr_str = self.__class__.__name__
         repr_str += f'(num_points={self.num_points},'
-        repr_str += f' sample_range={self.sample_range})'
+        repr_str += f' sample_range={self.sample_range},'
+        repr_str += f' replace={self.replace})'
 
         return repr_str
 

tests/test_data/test_pipelines/test_augmentations/test_transforms_3d.py

@@ -722,7 +722,7 @@ def test_points_sample():
         points.copy(), points_dim=4).convert_to(Coord3DMode.CAM, rect @ Trv2c)
     num_points = 20
     sample_range = 40
-    input_dict = dict(points=points)
+    input_dict = dict(points=points.clone())
 
     point_sample = PointSample(
         num_points=num_points, sample_range=sample_range)
@@ -736,6 +736,17 @@ def test_points_sample():
     assert np.allclose(sampled_pts.tensor.numpy(), expected_pts)
 
     repr_str = repr(point_sample)
-    expected_repr_str = f'PointSample(num_points={num_points},'\
-                        + f' sample_range={sample_range})'
+    expected_repr_str = f'PointSample(num_points={num_points}, ' \
+                        f'sample_range={sample_range}, ' \
+                        'replace=False)'
     assert repr_str == expected_repr_str
+
+    # test when number of far points are larger than number of sampled points
+    np.random.seed(0)
+    point_sample = PointSample(num_points=2, sample_range=sample_range)
+    input_dict = dict(points=points.clone())
+    sampled_pts = point_sample(input_dict)['points']
+
+    select_idx = np.array([449, 444])
+    expected_pts = points.tensor.numpy()[select_idx]
+    assert np.allclose(sampled_pts.tensor.numpy(), expected_pts)

tests/test_data/test_pipelines/test_indoor_sample.py

@@ -57,7 +57,9 @@ def test_indoor_sample():
     sunrgbd_choices = np.array([2, 8, 4, 9, 1])
     sunrgbd_points_result = sunrgbd_results['points'].tensor.numpy()
     repr_str = repr(sunrgbd_sample_points)
-    expected_repr_str = 'PointSample(num_points=5, sample_range=None)'
+    expected_repr_str = 'PointSample(num_points=5, ' \
+                        'sample_range=None, ' \
+                        'replace=False)'
     assert repr_str == expected_repr_str
     assert np.allclose(sunrgbd_point_cloud[sunrgbd_choices],
                        sunrgbd_points_result)