基于融合采樣和圖網(wǎng)絡(luò)的三維目標(biāo)檢測

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中圖分類號：TP391.41 文獻(xiàn)標(biāo)志碼：A 文章編號：1007-2683（2025）02-0042-11

Abstract：Inthe3Dtargetdetection technologybasedonpointcloud，there areproblems likehighcostof pointcloudcalculation andlargegap betwee targetscales，whichleadtolowtargetdetectioneficiency.Inresponse，thispaperproposesa3Dbject detectionalgorithmbasedonfusionsamplingandgraphetworks.Firstly，thepointcloudfusionsamplingtechnologyisintroducedto sampletheoriginalpointcloudtoeducethecomputatioalcomplexitySecondlytheK-NNalgorithisusedtoconstructtheapof thesampledpointcloud，andsub-imagesamplingisintroducedtosolvetheproblemofover-smothgraphconvolution.Finally，the featuresofgraphnodesareupdatedthroughfeatureinteraction toimprovethefeatureextractionabilityofthenetwork，thereby improvingthetargetdetectionefect.ThisstudyconductedexperimentsontheKIT3Ddataset.Comparedwith thebenchmarkmodel Point-GNN，the detection accuracy of the car target was improved by 3.89 % . In the simple scene and the medium difficulty scene，the detection accuracy of the cyclist target is increased by 6，60% and 4.36% ，respectively.

Keywords：point cloud; 3D object detection；graph neural network；fusion sampling；feature fusion

0 引言

在自動駕駛系統(tǒng)中，激光雷達(dá)作為環(huán)境感知設(shè)備被廣泛應(yīng)用。（剩余17257字）