Distance-dependent Feature Alignment and Selection for Imbalance 3D Point Cloud Object Detection

Although pillar-based 3D object detection methods can balance the performance and inference speed, the inconsistent object features caused by dramatic sparsity drops of LiDAR point clouds sabotage the detection accuracy. We present a novel and efficient plug-in method, SVDnet, to improve the state-of-the-art pillar-based models. First, a novel low-rank objective loss is introduced to extract distance-aware vehicle features and suppress the other variations. Next, we alleviated the remaining feature inconsistency caused by object positions with two strategies. One is a Distance Alignment Ratio-generation Network (DARN), which fuses multi-scale features by distance-adaptive ratios. The other is a position attention network that modulates features based on positions. Our results on the KITTI dataset show that SVDnet improves the pillar methods and outperforms the other plug-in strategies in accuracy and speed.