PV-SSD: A Multi-Modal Point Cloud Feature Fusion Method for Projection Features and Variable Receptive Field Voxel Features

arXiv:2308.06791v5 Announce Type: replace
Abstract: LiDAR-based 3D object detection and classification is crucial for autonomous driving. However, real-time inference from extremely sparse 3D data is a formidable challenge. To address this problem, a typical class of approaches transforms the point cloud cast into a regular data representation (voxels or projection maps). Then, it performs feature extraction with convolutional neural networks. However, such methods often result in a certain degree of information loss due to down-sampling or over-compression of feature information. …

3d object 3d object detection abstract arxiv autonomous autonomous driving challenge class classification cloud cs.cv data detection driving feature features fusion however inference lidar modal multi-modal object projection real-time ssd type voxel