We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture. While object detection with transformers has been an active field of research, it has proved difficult to apply such models to point clouds that span a large area, e.g. those that are common in autonomous driving, with lidar or radar data. TransLPC is able to remedy these issues: The structure of the transformer model is modified to allow for larger input sequence lengths, which are sufficient for large point clouds. Besides this, we propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries. The queries are repositioned between layers, moving them closer to the bounding box they are estimating, in an efficient manner. This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data. Besides this, the proposed method is compatible with existing transformer-based solutions that require object detection, e.g. for joint multi-object tracking and detection, and enables them to be used in conjunction with large point clouds.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Transformers for Object Detection in Large Point Clouds


    Contributors:


    Publication date :

    2022-10-08


    Size :

    1847847 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Transformers for Multi-Object Tracking on Point Clouds

    Ruppel, Felicia / Faion, Florian / Glaser, Claudius et al. | IEEE | 2022


    TOP-DOWN OBJECT DETECTION FROM LIDAR POINT CLOUDS

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

    Free access

    Weakly Supervised Point Clouds Transformer for 3D Object Detection

    Tang, Zuojin / Sun, Bo / Ma, Tongwei et al. | IEEE | 2022


    Exploiting Label Uncertainty for Enhanced 3D Object Detection From Point Clouds

    Sun, Yang / Lu, Bin / Liu, Yonghuai et al. | IEEE | 2024


    Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds

    Tian, Yonglin / Huang, Lichao / Yu, Hui et al. | IEEE | 2022