3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding. Nevertheless, an overlooked practical issue in existing methods is the ambiguity in training sample allocation based on box Intersection over Union (IoUbox). This problem impedes further enhancements in the performance of anchor-based LiDAR 3D object detectors. To tackle this challenge, this paper introduces a new training sample selection method that utilizes point cloud distribution for anchor sample quality measurement, named Point Assisted Sample Selection (PASS). This method has undergone rigorous evaluation on four widely utilized datasets. Experimental results demonstrate that the application of PASS elevates the average precision of anchor-based LiDAR 3D object detectors to a novel state-of-the-art, thereby proving the effectiveness of the proposed approach. The codes will be made available at https://github.com/ XJTU-Haolin/Point_Assisted_Sample_Selection.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Leveraging Anchor-Based LiDAR 3D Object Detection via Point Assisted Sample Selection


    Contributors:


    Publication date :

    2025-06-01


    Size :

    3860484 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    TOP-DOWN OBJECT DETECTION FROM LIDAR POINT CLOUDS

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | European Patent Office | 2024

    Free access

    TOP-DOWN OBJECT DETECTION FROM LIDAR POINT CLOUDS

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | European Patent Office | 2021

    Free access

    Top-down object detection from LiDAR point clouds

    SMOLYANSKIY NIKOLAI / OLDJA RYAN / CHEN KE et al. | European Patent Office | 2024

    Free access

    Leveraging Smooth Deformation Augmentation for LiDAR Point Cloud Semantic Segmentation

    Qiu, Shoumeng / Chen, Jie / Lai, Chenghang et al. | IEEE | 2024

    Free access