Density-Adaptive Sampling for Heterogeneous Point Cloud Object Segmentation in Autonomous Vehicle Applications

Robust understanding of the driving scene is among the key steps for accurate object detection and reliable autonomous driving. Accomplishing these tasks with a high level of precision, however, is not trivial. One of the challenges come from dealing with the heterogeneous density distribution and massively imbalanced class representation in the point cloud data, making the crude implementation of deep learning architectures for point cloud data from other domains less effective. In this paper, we propose a density-adaptive sampling method that is capable of dealing with the point density problem while preserving point-object representation. The method works by balancing the point density of pre-gridded point cloud data using oversampling, and then empirically sample points from the balanced grid. Using the KITTI Vision 3D Benchmark dataset for point cloud segmentation and PointCNN as the classifier of choice, our proposal provides superior results compared to the original PointCNN implementation, improving the performance from 82.73% using voxel-based sampling to 92.25% using our proposed density-adaptive sampling in terms of per class accuracy.