Density-Net: A Density-Aware Network for 3D Object Detection

To address the problem of density imbalance between nearby and faraway regions in point clouds, we propose a density-aware 3D single-stage object detector named Density-Net. First, a new 3D data augmentation method is designed to generate low-density areas by using the DBSCAN cluster method to acquire the cluster of point clouds, then by setting several different downsample ratios to stimulate sparse regions. By this way, there will be many sparse regions in point clouds. To restore the representative information, we propose Density-Set-Abstraction (Density-SA) to harmonize the high-level representative feature and low-level spatial feature, which boosts the final object detection performance. Moreover, we design a new Mask Sample branch to accomplish point sampling based on point-wise annotations, which increases the recall rate of meaningful points. We evaluate it on the widely used KITTI dataset. The experiments demonstrate that our method outperforms the well-established and highly-optimized 3DSSD (implemented by MMdetection3D) baseline 2.2% APs in moderate difficulty setting. At the same time, the runtime performance is also on par with 3DSSD.