Semantic-aware 3D-voxel CenterNet for point cloud object detection

Abstract

It is still challenging to accurately detect the small point cloud objects in the density-varying scenes, such as autonomous driving scenes where the measurements in nearby regions are much more than they in farther-away regions. Most previous methods detect objects with uniformly sampled key points, which may miss small objects. We propose a semantic-aware 3D-voxel CenterNet for 3D object detection (SA-Voxel-CenterNet) to address the above challenge. Specifically, SA-Voxel-CenterNet contains a cluster-enhanced center regression module (CCRM) and a semantic-aware proposal refinement module (SPRM). CCRM designs a voxel-wise semantic class segmentation branch and a center offset learning branch to extract centers of objects based on an improved Connected Component Analysis algorithm. Besides, SPRM generates the interesting region around each candidate with a predefined size. The experiments on the KITTI benchmark show that SA-Voxel-CenterNet delivers high detection accuracy, especially for the small object, e.g., cyclist, achieves state-of-the-art performance.