计算机与数字工程2025，Vol.53Issue(4)：980-983,4.DOI:10.3969/j.issn.1672-9722.2025.04.011
基于图网络和体素的三维目标检测
3D Object Detection Based on Graph Network and Voxel
摘要
Abstract
The voxel-based 3D object detection model outperforms the graph-based model in terms of speed and detection ac-curacy,but the use of average pooling operation during voxelization leads to loss of detail information,which degrades the perfor-mance of the model to some extent.The proposed method uses a graph network to explicitly construct the topology to capture local point cloud detail information during voxelization to solve the information loss in voxelization,and achieves a balance of speed and detection accuracy by cropping the voxel backbone network.The proposed method is experimented on the KITTI,a publicly avail-able 3D object detection database,for the detection of car class objects,and achieves 84.85%average precision(AP)detection re-sults,which exceedes some advanced 3D object detection models.关键词
三维目标检测/图网络/体素化/特征处理Key words
3D object detection/graph network/voxelization/feature processing分类
信息技术与安全科学引用本文复制引用
黄伟,金忠..基于图网络和体素的三维目标检测[J].计算机与数字工程,2025,53(4):980-983,4.基金项目
国家自然科学基金项目(编号:61872188,61972204)资助. （编号:61872188,61972204）
