[Feature] SemanticKITTI Dataset

mmdet3d/datasets/__init__.py

@@ -11,6 +11,7 @@
  ObjectSample, PointShuffle, PointsRangeFilter,
  RandomFlip3D, VoxelBasedPointSampler)
 from .scannet_dataset import ScanNetDataset
+from .semantickitti_dataset import SemanticKITTIDataset
 from .sunrgbd_dataset import SUNRGBDDataset
 from .waymo_dataset import WaymoDataset
 
@@ -21,7 +22,7 @@
  'RandomFlip3D', 'ObjectNoise', 'GlobalRotScaleTrans', 'PointShuffle',
  'ObjectRangeFilter', 'PointsRangeFilter', 'Collect3D',
  'LoadPointsFromFile', 'NormalizePointsColor', 'IndoorPointSample',
- 'LoadAnnotations3D', 'SUNRGBDDataset', 'ScanNetDataset', 'Custom3DDataset',
- 'LoadPointsFromMultiSweeps', 'WaymoDataset', 'BackgroundPointsFilter',
- 'VoxelBasedPointSampler'
+ 'LoadAnnotations3D', 'SUNRGBDDataset', 'ScanNetDataset',
+ 'SemanticKITTIDataset', 'Custom3DDataset', 'LoadPointsFromMultiSweeps',
+ 'WaymoDataset', 'BackgroundPointsFilter', 'VoxelBasedPointSampler'
 ]

mmdet3d/datasets/pipelines/loading.py

@@ -419,6 +419,8 @@ class LoadAnnotations3D(LoadAnnotations):
  Defaults to False.
  poly2mask (bool, optional): Whether to convert polygon annotations
  to bitmasks. Defaults to True.
+ seg_3d_dtype (dtype, optional): Dtype of 3D semantic masks.
+ Defaults to int64
  file_client_args (dict): Config dict of file clients, refer to
  https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py
  for more details.
@@ -434,6 +436,7 @@ def __init__(self,
  with_mask=False,
  with_seg=False,
  poly2mask=True,
+ seg_3d_dtype='int',
  file_client_args=dict(backend='disk')):
  super().__init__(
  with_bbox,
@@ -446,6 +449,7 @@ def __init__(self,
  self.with_label_3d = with_label_3d
  self.with_mask_3d = with_mask_3d
  self.with_seg_3d = with_seg_3d
+ self.seg_3d_dtype = seg_3d_dtype
 
  def _load_bboxes_3d(self, results):
  """Private function to load 3D bounding box annotations.
@@ -513,7 +517,8 @@ def _load_semantic_seg_3d(self, results):
  try:
  mask_bytes = self.file_client.get(pts_semantic_mask_path)
  # add .copy() to fix read-only bug
- pts_semantic_mask = np.frombuffer(mask_bytes, dtype=np.int).copy()
+ pts_semantic_mask = np.frombuffer(
+ mask_bytes, dtype=self.seg_3d_dtype).copy()
  except ConnectionError:
  mmcv.check_file_exist(pts_semantic_mask_path)
  pts_semantic_mask = np.fromfile(

mmdet3d/datasets/semantickitti_dataset.py

@@ -0,0 +1,80 @@
+from os import path as osp
+
+from mmdet.datasets import DATASETS
+from .custom_3d import Custom3DDataset
+
+
+@DATASETS.register_module()
+class SemanticKITTIDataset(Custom3DDataset):
+ r"""SemanticKITTI Dataset.
+
+ This class serves as the API for experiments on the SemanticKITTI Dataset
+ Please refer to <http://www.semantic-kitti.org/dataset.html>`_
+ for data downloading
+
+ Args:
+ data_root (str): Path of dataset root.
+ ann_file (str): Path of annotation file.
+ pipeline (list[dict], optional): Pipeline used for data processing.
+ Defaults to None.
+ classes (tuple[str], optional): Classes used in the dataset.
+ Defaults to None.
+ modality (dict, optional): Modality to specify the sensor data used
+ as input. Defaults to None.
+ box_type_3d (str, optional): NO 3D box for this dataset.
+ You can choose any type
+ Based on the `box_type_3d`, the dataset will encapsulate the box
+ to its original format then converted them to `box_type_3d`.
+ Defaults to 'LiDAR' in this dataset. Available options includes
+
+ - 'LiDAR': Box in LiDAR coordinates.
+ - 'Depth': Box in depth coordinates, usually for indoor dataset.
+ - 'Camera': Box in camera coordinates.
+ filter_empty_gt (bool, optional): Whether to filter empty GT.
+ Defaults to True.
+ test_mode (bool, optional): Whether the dataset is in test mode.
+ Defaults to False.
+ """
+ CLASSES = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
+ 'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
+ 'sidewalk', 'other-ground', 'building', 'fence', 'vegetation',
+ 'trunck', 'terrian', 'pole', 'traffic-sign')
+
+ def __init__(self,
+ data_root,
+ ann_file,
+ pipeline=None,
+ classes=None,
+ modality=None,
+ box_type_3d='Lidar',
+ filter_empty_gt=False,
+ test_mode=False):
+ super().__init__(
+ data_root=data_root,
+ ann_file=ann_file,
+ pipeline=pipeline,
+ classes=classes,
+ modality=modality,
+ box_type_3d=box_type_3d,
+ filter_empty_gt=filter_empty_gt,
+ test_mode=test_mode)
+
+ def get_ann_info(self, index):
+ """Get annotation info according to the given index.
+
+ Args:
+ index (int): Index of the annotation data to get.
+
+ Returns:
+ dict: annotation information consists of the following keys:
+
+ - pts_semantic_mask_path (str): Path of semantic masks.
+ """
+ # Use index to get the annos, thus the evalhook could also use this api
+ info = self.data_infos[index]
+
+ pts_semantic_mask_path = osp.join(self.data_root,
+ info['pts_semantic_mask_path'])
+
+ anns_results = dict(pts_semantic_mask_path=pts_semantic_mask_path)
+ return anns_results

requirements/runtime.txt

@@ -2,6 +2,7 @@ lyft_dataset_sdk
 networkx>=2.2,<2.3
 # we may unlock the verion of numba in the future
 numba==0.48.0
+numpy<1.20.0
 nuscenes-devkit
 plyfile
 scikit-image

tests/test_dataset/test_semantickitti_dataset.py

@@ -0,0 +1,52 @@
+import numpy as np
+
+from mmdet3d.datasets import SemanticKITTIDataset
+
+
+def test_getitem():
+ np.random.seed(0)
+ root_path = './tests/data/semantickitti/'
+ ann_file = './tests/data/semantickitti/semantickitti_infos.pkl'
+ class_names = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
+ 'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
+ 'sidewalk', 'other-ground', 'building', 'fence',
+ 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign')
+ pipelines = [
+ dict(
+ type='LoadPointsFromFile',
+ coord_type='LIDAR',
+ shift_height=True,
+ load_dim=4,
+ use_dim=[0, 1, 2]),
+ dict(
+ type='LoadAnnotations3D',
+ with_bbox_3d=False,
+ with_label_3d=False,
+ with_mask_3d=False,
+ with_seg_3d=True,
+ seg_3d_dtype=np.int32),
+ dict(
+ type='RandomFlip3D',
+ sync_2d=False,
+ flip_ratio_bev_horizontal=1.0,
+ flip_ratio_bev_vertical=1.0),
+ 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',
+ 'pts_semantic_mask',
+ ],
+ meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
+ ]
+
+ semantickitti_dataset = SemanticKITTIDataset(root_path, ann_file,
+ pipelines)
+ data = semantickitti_dataset[0]
+ assert data['points']._data.shape[0] == data[
+ 'pts_semantic_mask']._data.shape[0]