软件导刊2025，Vol.24Issue(3)：137-144,8.DOI:10.11907/rjdk.241042
基于点云自适应采样与点注意力的3D目标检测
3D Object Detection Using Point Cloud Adaptive Sampling and Point-Attention
摘要
Abstract
Point cloud down-sampling and feature extraction are indispensable steps in the LiDAR point clouds-based 3D object detection methods for automatic driving.However,the traditional down-sampling methods are indiscriminate for all data points in the point cloud,which makes the points on the object evenly diluted,and the object with fewer points may lose all its points.At the same time,the current point cloud feature extraction method only retains the maximum value of the same dimension in the feature,which can't make full use of the point cloud feature.To address these issues,proposes a 3D object detection method based on LiDAR point cloud adaptive down-sampling and point attention feature aggregation.This method acquires category information of data points by utilizing a learning-based adaptive down-sam-pling operation,thus realizing categorical down-sampling of LiDAR point clouds.At the same time,the point attention feature aggregation method is used to aggregate the features of sampling points according to the attention coefficient between points,which can make full use of the feature information of the point cloud.In KITTI data set,the detection accuracy of cars,pedestrians and cyclists reached 78.49%、44.82%and 63.59%respectively,which verified the effectiveness of this method.Compared with other methods,the detection accuracy of the object with fewer points is significantly improved.关键词
3D目标检测/激光雷达点云/自适应下采样/点注意力Key words
3D object detection/LiDAR point cloud/adaptive down-sampling/point attention分类
信息技术与安全科学引用本文复制引用
白彦彪,黄影平,梁振明,崔健源,胡雄飞..基于点云自适应采样与点注意力的3D目标检测[J].软件导刊,2025,24(3):137-144,8.基金项目
国家自然科学基金项目(62276167) （62276167）
上海市自然科学基金项目(20ZR1437900) （20ZR1437900）
