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LiDAR-based 3D object detection pushes forward an immense influence onautonomous vehicles. Due to the limitation of the intrinsic properties ofLiDAR, fewer points are collected at the objects farther away from the sensor.This imbalanced density of point clouds degrades the detection accuracy but isgenerally neglected by previous works. To address the challenge, we propose anovel two-stage 3D object detection framework, named SIENet. Specifically, wedesign the Spatial Information Enhancement (SIE) module to predict the spatialshapes of the foreground points within proposals, and extract the structureinformation to learn the representative features for further box refinement.The predicted spatial shapes are complete and dense point sets, thus theextracted structure information contains more semantic representation. Besides,we design the Hybrid-Paradigm Region Proposal Network (HP-RPN) which includesmultiple branches to learn discriminate features and generate accurateproposals for the SIE module. Extensive experiments on the KITTI 3D objectdetection benchmark show that our elaborately designed SIENet outperforms thestate-of-the-art methods by a large margin.

SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud