Spatial information enhancement network for 3D object detection from point cloud

Ziyu Li, Yuncong Yao, Zhibin Quan, Jin Xie, Wankou Yang

Abstract: Highlights•To address the density imbalanced problem in point clouds, we propose a novel spatial information enhancement module (SIE) to predict the dense shapes of point sets in candidate boxes, and learn the structure information to improve the ability of feature representation.•We present a hybrid-paradigm region proposal network (HP-RPN) for more effective multi-scale feature extraction and high-recall proposal generation.•With the structure information as guidance, our elaborately designed SIENet achieves the state-of-the-art performance of 3D object detection on the KITTI benchmark.•The encouraging experimental results also demonstrate the outstanding improvement in far-range object detection.