The content discusses the challenges faced by existing LiDAR-based 3D object detection methods in adapting to unseen data distributions. It introduces CMDA, which utilizes cross-modal knowledge interaction and domain-adaptive self-training to improve performance across various benchmarks like nuScenes, Waymo, and KITTI. The approach significantly outperforms state-of-the-art methods in unsupervised domain adaptation tasks.
Recent advancements in LiDAR-based 3D object detection have shown promise but struggle with generalization to new domains. Existing approaches focus on geometric information from point clouds, lacking semantic cues from images. To address this gap, CMDA leverages visual semantic cues from images to bridge domain gaps in Bird’s Eye View representations.
By introducing CMKI and CDAN techniques, CMDA guides the model to generate highly informative and domain-adaptive features for novel data distributions. The framework effectively overcomes domain shift issues and achieves state-of-the-art performance in UDA tasks for 3DOD.
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by Gyusam Chang... at arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03721.pdfDeeper Inquiries