Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning


    Contributors:
    Zhang, Yanan (author) / Zhou, Chao (author) / Huang, Di (author)

    Published in:

    Publication date :

    2024-11-01


    Size :

    4067502 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Improving Cross-Domain Semi-Supervised Object Detection with Adversarial Domain Adaptation

    Menke, Maximilian / Wenzel, Thomas / Schwung, Andreas | IEEE | 2023


    Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

    Gong, B. / Grauman, K. / Sha, F. | British Library Online Contents | 2014


    MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection

    Tsai, Darren / Berrio, Julie Stephany / Shan, Mao et al. | IEEE | 2023



    Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation

    Michieli, Umberto / Biasetton, Matteo / Agresti, Gianluca et al. | IEEE | 2020