Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection
This work addresses a domain-specific problem for LiDAR-based 3D object detection, offering an incremental improvement in representation efficiency.
The paper tackles the problem of efficient point cloud representation for 3D object detection by introducing a hybrid Voxel-Pillar Fusion network (VPF) that combines voxel and pillar features, achieving competitive performance with real-time inference speeds on nuScenes and Waymo Open Dataset.
Efficient representation of point clouds is fundamental for LiDAR-based 3D object detection. While recent grid-based detectors often encode point clouds into either voxels or pillars, the distinctions between these approaches remain underexplored. In this paper, we quantify the differences between the current encoding paradigms and highlight the limited vertical learning within. To tackle these limitations, we introduce a hybrid Voxel-Pillar Fusion network (VPF), which synergistically combines the unique strengths of both voxels and pillars. Specifically, we first develop a sparse voxel-pillar encoder that encodes point clouds into voxel and pillar features through 3D and 2D sparse convolutions respectively, and then introduce the Sparse Fusion Layer (SFL), facilitating bidirectional interaction between sparse voxel and pillar features. Our efficient, fully sparse method can be seamlessly integrated into both dense and sparse detectors. Leveraging this powerful yet straightforward framework, VPF delivers competitive performance, achieving real-time inference speeds on the nuScenes and Waymo Open Dataset. The code will be available.