[Enhance] Sampling points based on distance metric

configs/3dssd/3dssd_4x4_kitti-3d-car.py

@@ -51,7 +51,7 @@
  rot_range=[-0.78539816, 0.78539816],
  scale_ratio_range=[0.9, 1.1]),
  dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
- dict(type='IndoorPointSample', num_points=16384),
+ dict(type='PointSample', num_points=16384),
  dict(type='DefaultFormatBundle3D', class_names=class_names),
  dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
@@ -77,7 +77,7 @@
  dict(type='RandomFlip3D'),
  dict(
  type='PointsRangeFilter', point_cloud_range=point_cloud_range),
- dict(type='IndoorPointSample', num_points=16384),
+ dict(type='PointSample', num_points=16384),
  dict(
  type='DefaultFormatBundle3D',
  class_names=class_names,

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

@@ -20,7 +20,7 @@
  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'])
 ]
@@ -47,7 +47,7 @@
  sync_2d=False,
  flip_ratio_bev_horizontal=0.5,
  ),
- dict(type='IndoorPointSample', num_points=20000),
+ dict(type='PointSample', num_points=20000),
  dict(
  type='DefaultFormatBundle3D',
  class_names=class_names,

configs/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class.py

@@ -187,7 +187,7 @@
  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',
@@ -225,7 +225,7 @@
  sync_2d=False,
  flip_ratio_bev_horizontal=0.5,
  ),
- dict(type='IndoorPointSample', num_points=20000),
+ dict(type='PointSample', num_points=20000),
  dict(
  type='DefaultFormatBundle3D',
  class_names=class_names,

docs/tutorials/config.md

@@ -203,7 +203,7 @@ train_pipeline = [ # Training pipeline, refer to mmdet3d.datasets.pipelines for
  valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
  36, 39), # all valid categories ids
  max_cat_id=40), # max possible category id in input segmentation mask
- dict(type='IndoorPointSample', # Sample indoor points, refer to mmdet3d.datasets.pipelines.indoor_sample for more details
+ dict(type='PointSample', # Sample points, refer to mmdet3d.datasets.pipelines.transforms_3d for more details
  num_points=40000), # Number of points to be sampled
  dict(type='IndoorFlipData', # Augmentation pipeline that flip points and 3d boxes
  flip_ratio_yz=0.5, # Probability of being flipped along yz plane
@@ -232,7 +232,7 @@ test_pipeline = [ # Testing pipeline, refer to mmdet3d.datasets.pipelines for m
  shift_height=True, # Whether to use shifted height
  load_dim=6, # The dimension of the loaded points
  use_dim=[0, 1, 2]), # Which dimensions of the points to be used
- dict(type='IndoorPointSample', # Sample indoor points, refer to mmdet3d.datasets.pipelines.indoor_sample for more details
+ dict(type='PointSample', # Sample points, refer to mmdet3d.datasets.pipelines.transforms_3d for more details
  num_points=40000), # Number of points to be sampled
  dict(
  type='DefaultFormatBundle3D', # Default format bundle to gather data in the pipeline, refer to mmdet3d.datasets.pipelines.formating for more details
@@ -286,7 +286,7 @@ 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,
@@ -325,7 +325,7 @@ 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',
@@ -350,7 +350,7 @@ 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/core/points/base_points.py

@@ -281,6 +281,8 @@ def __getitem__(self, item):
  Nonzero elements in the vector will be selected.
  4. `new_points = points[3:11, vector]`:
  return a slice of points and attribute dims.
+ 5. `new_points = points[4:12, 2]`:
+ return a slice of points with single attribute.
  Note that the returned Points might share storage with this Points,
  subject to Pytorch's indexing semantics.
 
@@ -303,6 +305,10 @@ def __getitem__(self, item):
  item = list(item)
  item[1] = list(range(start, stop, step))
  item = tuple(item)
+ elif isinstance(item[1], int):
+ item = list(item)
+ item[1] = [item[1]]
+ item = tuple(item)
  p = self.tensor[item[0], item[1]]
 
  keep_dims = list(

mmdet3d/datasets/__init__.py

@@ -7,14 +7,17 @@
 from .lyft_dataset import LyftDataset
 from .nuscenes_dataset import NuScenesDataset
 from .nuscenes_mono_dataset import NuScenesMonoDataset
+# yapf: disable
 from .pipelines import (BackgroundPointsFilter, GlobalAlignment,
  GlobalRotScaleTrans, IndoorPatchPointSample,
  IndoorPointSample, LoadAnnotations3D,
  LoadPointsFromFile, LoadPointsFromMultiSweeps,
  NormalizePointsColor, ObjectNameFilter, ObjectNoise,
- ObjectRangeFilter, ObjectSample, PointShuffle,
- PointsRangeFilter, RandomDropPointsColor, RandomFlip3D,
- RandomJitterPoints, VoxelBasedPointSampler)
+ ObjectRangeFilter, ObjectSample, PointSample,
+ PointShuffle, PointsRangeFilter, RandomDropPointsColor,
+ RandomFlip3D, RandomJitterPoints,
+ VoxelBasedPointSampler)
+# yapf: enable
 from .s3dis_dataset import S3DISSegDataset
 from .scannet_dataset import ScanNetDataset, ScanNetSegDataset
 from .semantickitti_dataset import SemanticKITTIDataset
@@ -30,9 +33,10 @@
  'ObjectNoise', 'GlobalRotScaleTrans', 'PointShuffle', 'ObjectRangeFilter',
  'PointsRangeFilter', 'Collect3D', 'LoadPointsFromFile', 'S3DISSegDataset',
  'NormalizePointsColor', 'IndoorPatchPointSample', 'IndoorPointSample',
- 'LoadAnnotations3D', 'GlobalAlignment', 'SUNRGBDDataset', 'ScanNetDataset',
- 'ScanNetSegDataset', 'SemanticKITTIDataset', 'Custom3DDataset',
- 'Custom3DSegDataset', 'LoadPointsFromMultiSweeps', 'WaymoDataset',
- 'BackgroundPointsFilter', 'VoxelBasedPointSampler', 'get_loading_pipeline',
- 'RandomDropPointsColor', 'RandomJitterPoints', 'ObjectNameFilter'
+ 'PointSample', 'LoadAnnotations3D', 'GlobalAlignment', 'SUNRGBDDataset',
+ 'ScanNetDataset', 'ScanNetSegDataset', 'SemanticKITTIDataset',
+ 'Custom3DDataset', 'Custom3DSegDataset', 'LoadPointsFromMultiSweeps',
+ 'WaymoDataset', 'BackgroundPointsFilter', 'VoxelBasedPointSampler',
+ 'get_loading_pipeline', 'RandomDropPointsColor', 'RandomJitterPoints',
+ 'ObjectNameFilter'
 ]

mmdet3d/datasets/pipelines/__init__.py

@@ -9,19 +9,20 @@
 from .transforms_3d import (BackgroundPointsFilter, GlobalAlignment,
  GlobalRotScaleTrans, IndoorPatchPointSample,
  IndoorPointSample, ObjectNameFilter, ObjectNoise,
- ObjectRangeFilter, ObjectSample, PointShuffle,
- PointsRangeFilter, RandomDropPointsColor,
- RandomFlip3D, RandomJitterPoints,
- VoxelBasedPointSampler)
+ ObjectRangeFilter, ObjectSample, PointSample,
+ PointShuffle, PointsRangeFilter,
+ RandomDropPointsColor, RandomFlip3D,
+ RandomJitterPoints, VoxelBasedPointSampler)
 
 __all__ = [
  'ObjectSample', 'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans',
  'PointShuffle', 'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
  'Compose', 'LoadMultiViewImageFromFiles', 'LoadPointsFromFile',
  'DefaultFormatBundle', 'DefaultFormatBundle3D', 'DataBaseSampler',
  'NormalizePointsColor', 'LoadAnnotations3D', 'IndoorPointSample',
- 'PointSegClassMapping', 'MultiScaleFlipAug3D', 'LoadPointsFromMultiSweeps',
- 'BackgroundPointsFilter', 'VoxelBasedPointSampler', 'GlobalAlignment',
- 'IndoorPatchPointSample', 'LoadImageFromFileMono3D', 'ObjectNameFilter',
- 'RandomDropPointsColor', 'RandomJitterPoints'
+ 'PointSample', 'PointSegClassMapping', 'MultiScaleFlipAug3D',
+ 'LoadPointsFromMultiSweeps', 'BackgroundPointsFilter',
+ 'VoxelBasedPointSampler', 'GlobalAlignment', 'IndoorPatchPointSample',
+ 'LoadImageFromFileMono3D', 'ObjectNameFilter', 'RandomDropPointsColor',
+ 'RandomJitterPoints'
 ]

mmdet3d/datasets/pipelines/transforms_3d.py

@@ -818,45 +818,60 @@ def __repr__(self):
 
 
 @PIPELINES.register_module()
-class IndoorPointSample(object):
- """Indoor point sample.
+class PointSample(object):
+ """Point sample.
 
  Sampling data to a certain number.
 
  Args:
- name (str): Name of the dataset.
  num_points (int): Number of points to be sampled.
+ sample_range (float, optional): The range where to sample points.
  """
 
- def __init__(self, num_points):
+ def __init__(self, num_points, sample_range=None):
  self.num_points = num_points
-
- def points_random_sampling(self,
- points,
- num_samples,
- replace=None,
- return_choices=False):
+ self.sample_range = sample_range
+
+ def _points_random_sampling(self,
+ points,
+ num_samples,
+ sample_range=None,
+ replace=None,
+ return_choices=False):
  """Points random sampling.
 
  Sample points to a certain number.
 
  Args:
  points (np.ndarray | :obj:`BasePoints`): 3D Points.
  num_samples (int): Number of samples to be sampled.
- replace (bool): Whether the sample is with or without replacement.
- Defaults to None.
- return_choices (bool): Whether return choice. Defaults to False.
-
+ sample_range (float, optional): Indicating the range where the
+ 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.
+ Defaults to False.
  Returns:
  tuple[np.ndarray] | np.ndarray:
-
  - points (np.ndarray | :obj:`BasePoints`): 3D Points.
  - choices (np.ndarray, optional): The generated random samples.
  """
  if replace is None:
  replace = (points.shape[0] < num_samples)
- choices = np.random.choice(
- points.shape[0], num_samples, replace=replace)
+ point_range = range(len(points))
+ if sample_range is not None and not replace:
+ # Only sampling the near points when len(points) >= num_samples
+ depth = np.linalg.norm(points.tensor, axis=1)
+ far_inds = np.where(depth > sample_range)[0]
+ near_inds = np.where(depth <= sample_range)[0]
+ point_range = near_inds
+ num_samples -= len(far_inds)
+ choices = np.random.choice(point_range, num_samples, replace=replace)
+ if sample_range is not None and not replace:
+ choices = np.concatenate((far_inds, choices))
+ # Shuffle points after sampling
+ np.random.shuffle(choices)
  if return_choices:
  return points[choices], choices
  else:
@@ -867,14 +882,19 @@ def __call__(self, results):
 
  Args:
  input_dict (dict): Result dict from loading pipeline.
-
  Returns:
  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, choices = self.points_random_sampling(
- points, self.num_points, return_choices=True)
+ # 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:
+ assert isinstance(points, CameraPoints), \
+ 'Sampling based on distance is only appliable for CAMERA coord'
+ points, choices = self._points_random_sampling(
+ points, self.num_points, self.sample_range, return_choices=True)
  results['points'] = points
 
  pts_instance_mask = results.get('pts_instance_mask', None)
@@ -893,10 +913,29 @@ def __call__(self, results):
  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'(num_points={self.num_points},'
+ repr_str += f' sample_range={self.sample_range})'
+
  return repr_str
 
 
+@PIPELINES.register_module()
+class IndoorPointSample(PointSample):
+ """Indoor point sample.
+
+ Sampling data to a certain number.
+ NOTE: IndoorPointSample is deprecated in favor of PointSample
+
+ Args:
+ num_points (int): Number of points to be sampled.
+ """
+
+ def __init__(self, *args, **kwargs):
+ warnings.warn(
+ 'IndoorPointSample is deprecated in favor of PointSample')
+ super(IndoorPointSample, self).__init__(*args, **kwargs)
+
+
 @PIPELINES.register_module()
 class IndoorPatchPointSample(object):
  r"""Indoor point sample within a patch. Modified from `PointNet++ <https://

tests/test_data/test_datasets/test_scannet_dataset.py

@@ -32,7 +32,7 @@ def test_getitem():
  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=5),
+ dict(type='PointSample', num_points=5),
  dict(
  type='RandomFlip3D',
  sync_2d=False,

tests/test_data/test_datasets/test_sunrgbd_dataset.py

@@ -28,7 +28,7 @@ def _generate_sunrgbd_dataset_config():
  rot_range=[-0.523599, 0.523599],
  scale_ratio_range=[0.85, 1.15],
  shift_height=True),
- dict(type='IndoorPointSample', num_points=5),
+ dict(type='PointSample', num_points=5),
  dict(type='DefaultFormatBundle3D', class_names=class_names),
  dict(
  type='Collect3D',
@@ -73,7 +73,7 @@ def _generate_sunrgbd_multi_modality_dataset_config():
  rot_range=[-0.523599, 0.523599],
  scale_ratio_range=[0.85, 1.15],
  shift_height=True),
- dict(type='IndoorPointSample', num_points=5),
+ dict(type='PointSample', num_points=5),
  dict(type='DefaultFormatBundle3D', class_names=class_names),
  dict(
  type='Collect3D',

tests/test_data/test_pipelines/test_augmentations/test_test_augment_utils.py

@@ -17,7 +17,7 @@ def test_multi_scale_flip_aug_3D():
  'sync_2d': False,
  'flip_ratio_bev_horizontal': 0.5
  }, {
- 'type': 'IndoorPointSample',
+ 'type': 'PointSample',
  'num_points': 5
  }, {
  'type':