Spatial information enhancement network for 3D object detection from point cloud
作者：
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.
摘要
•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.
论文关键词：3D object detection,Autonomous vehicles,Point cloud,LiDAR sensor,3D shape completion
论文评审过程：Received 31 December 2021, Revised 19 March 2022, Accepted 2 April 2022, Available online 9 April 2022, Version of Record 13 April 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108684
