SSL-VoxPart: A Novel Solid-State LiDAR-Tailored Voxel Partition Approach for 3D Perception

Nico Leuze, Henry Schaub, Maximilian Hoh, Alfred Schöttl

Abstract: LiDAR-based 3D point cloud segmentation is a crucial component in the perception system of many applications that rely on a thorough 3D environment understanding. LiDAR point clouds are unstructured, sparsely scattered in 3D space and have non-uniform densities, which restricts the use of ordinary convolutional neural networks. Tackling the point clouds irregular format, several approaches are introduced that divide the 3D space into a discretised volumetric grid. However, voxelization inevitably abandons the 3D topology of point clouds and suffers from information loss. In this paper, we propose SSL-VoxPart, a novel approach of scan-pattern related partitioning for voxel-wise semantic segmentation suitable to MEMS-actuated Solid-State LiDARs. We argue scan-pattern related voxel partitioning is worth being considered by showing improvements in extracting semantic information for voxel-wise point cloud segmentation, while reducing lossy cell-label encoding. We specifically conduct to the problems of crowded scene analysis. Crowds represent locally high density distributions of people with heavy occlusion effects, demanding for fine-grained predictions. We introduce a SSL data generation pipeline tailored to surveillance scenarios and validate our proposed method on the resulting dataset.

External IDs:dblp:conf/ieeesensors/LeuzeSHS23