Light detection and ranging (LiDAR) is an essential sensor for three dimensional (3D) object detection via generating 3D point cloud of the surroundings, and it has been widely used in the various visual applications, especially autonomous driving. However, limited numbers of labeled LiDAR datasets brutally restrain the development of 3D object detector, and this situation breeds an urgent demand on data augmentation in this field. By far, most of the traditional methods reuse the labeled samples, while those unlabeled are hastily untaken. Motivated by this, we propose a Realistic Simulator based data augmentation (RS-Aug). It aims to construct augmented real scenes to enrich the diversity of training dataset. To train 3D object detector in a supervised learning way, the first step of RS-Aug is auto-annotation. Time-continuous LiDAR frames are used to construct the dense scene, which is beneficial to annotation and the subsequent rendering augmentation. However, 3D points with incorrect semantic labels are naturally gathered during multi-view reconstruction, causing the negative effect on auto-annotation. We propose an algorithm of cluster guided $k$ -nearest neighbor (c- $k$ NN). It emphasizes on de-nosing semantic labels of clustered points using distance and intensity constraints. Then, the next step of RS-Aug is rendering augmentation on the real scene. To enhance the rendering quality using collision and distance constraints with the less computation complexity, we propose a scheme of heuristic search (HS) based object insertion. It estimates the proper position of the inserted object from 2D bird’s eye view (BEV). Experiments demonstrate the de-noising accuracy of c- $k$ NN, rendering quality of HS based object insertion, and improvement of RS-Aug on object detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    RS-Aug: Improve 3D Object Detection on LiDAR With Realistic Simulator Based Data Augmentation


    Beteiligte:
    An, Pei (Autor:in) / Liang, Junxiong (Autor:in) / Ma, Jie (Autor:in) / Chen, Yanfei (Autor:in) / Wang, Liheng (Autor:in) / Yang, You (Autor:in) / Liu, Qiong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2023


    Format / Umfang :

    4974785 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Pattern-Aware Data Augmentation for LiDAR 3D Object Detection

    Hu, Jordan S.K. / Waslander, Steven L. | IEEE | 2021


    MARSIM: A light-weight point-realistic simulator for LiDAR-based UAVs

    Kong, Fanze / Liu, Xiyuan / Tang, Benxu et al. | ArXiv | 2022

    Freier Zugriff

    Evaluation of Point Cloud Data Augmentation for 3D-LiDAR Object Detection in Autonomous Driving

    Martins, Marta / Gomes, Iago P. / Wolf, Denis Fernando et al. | Springer Verlag | 2024


    LANGUAGE-BASED OBJECT DETECTION AND DATA AUGMENTATION

    SCHULTER SAMUEL / AICH ABHISHEK / GOPALKRISHNA VIJAY KUMAR BAIKAMPADY | Europäisches Patentamt | 2025

    Freier Zugriff

    CNN-based synthesis of realistic high-resolution LiDAR data

    Triess, Larissa T. / Peter, David / Rist, Christoph B. et al. | IEEE | 2019