Article(id=1309639710880919640, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20240211, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1759939200000, revisedDateStr=2025-10-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172039002, onlineDateStr=2026-09-23, pubDate=1771862400000, pubDateStr=2026-02-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172039002, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172039002, creator=13701087609, updateTime=1790172039002, updator=13701087609, issue=Issue{id=1309639699476606989, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='2', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1771862400000', pubDateStr='2026-02-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172036284, creator='13701087609', updateTime=1790232907749, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895012968661940, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895012968661941, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=32, endPage=38, ext={EN=ArticleExt(id=1309639711086440537, articleId=1309639710880919640, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=LiDAR-Based 3D Object Detection Algorithm Based on Improved PointPillars, columnId=1309639700797809024, journalTitle=Automobile Technology, columnName=Special Topic on LiDAR Perception and Multimodal 3D Object Detection Technology for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

To avoid the missing detection phenomenon caused by loss of local features due to the occlusion and truncation under complex traffic scenes, a LiDAR-based 3D object detection algorithm based on an improved PointPillars framework is proposed. First, the point cloud feature encoding network in PointPillars is enhanced to better preserve important structural information and the relationships within the point cloud data. Then, multi-scale point cloud features are extracted and aggregated through stacked sparse convolution modules and submanifold convolution modules. On the bird's-eye-view feature map that fuses these multi-scale features, 3D bounding boxes of objects are classified and regressed, improving the network's operational efficiency. Finally, experimental tests are conducted on the KITTI dataset. The results show that compared to the traditional PointPillars algorithm, the proposed 3D object detection method improves the Average Precision (AP) of 3D detection for challenging vehicle samples by 6.02 percentage points, and increases the AP for simple pedestrian and cyclist samples in the bird's-eye view by 3.35 and 3.18 percentage points respectively, effectively enhancing 3D object detection accuracy.

, authors=Lie Guo^{1, 2}, Liang Huang¹, Yibing Zhao¹, authorsList=Lie Guo, Liang Huang, Yibing Zhao, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639711312932954, articleId=1309639710880919640, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进PointPillars的激光雷达三维目标检测算法, columnId=1309639700932026754, journalTitle=汽车技术, columnName=激光雷达感知与自动驾驶多模态3D 目标检测技术专题, runingTitle=null, highlight=null, articleAbstract=

为解决复杂交通场景中目标因被遮挡、截断等造成局部特征丢失而产生的漏检问题，提出了一种基于改进PointPillars的激光雷达三维目标检测算法。首先，通过改进PointPillars中的点云特征编码网络，更好地保留点云数据中重要的结构信息及其相互关系，然后，通过堆叠的稀疏卷积模块和子流形卷积模块提取并聚合多尺度点云特征，在融合多尺度特征的鸟瞰特征图上对目标的三维检测框进行分类和回归，提升网络运行效率，最后，在KITTI数据集上进行了试验验证。结果表明：与传统PointPillars算法相比，所提出的三维目标检测算法对车辆困难检测样本的3D检测平均精度（AP）提高了6.02百分点，对鸟瞰视角下行人和骑行者简单样本的AP分别提高了3.35百分点和3.18百分点，有效提升了三维目标检测精度。

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