We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they often overfit to specific domain biases, leading to suboptimal performance in various sensor setups and environments. Existing methods typically focus on adapting a single detector to the target domain, overlooking the fact that different detectors possess distinct expertise on different unseen domains. MS3D leverages this by combining different pre-trained detectors from multiple source domains and incorporating temporal information to produce high-quality pseudo-labels for fine-tuning. Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors. MS3D exhibits greater robustness to domain shifts and produces accurate pseudo-labels over greater distances, making it well-suited for high-to-low beam domain adaptation and vice versa. Our method achieved state-of-the-art performance on all evaluated datasets, and we demonstrate that the choice of pre-trained source detectors has minimal impact on the self-training result, making MS3D suitable for real-world applications.

翻译：我们提出了多源3D（MS3D），一种用于3D目标检测中无监督域适应的新型自训练流程。尽管3D检测器具有显著的精度，但它们常常过拟合特定的域偏差，导致在不同传感器配置和环境中的性能次优。现有方法通常专注于将单一检测器适应到目标域，忽略了不同检测器在不同未知域中拥有独特专长的事实。MS3D通过结合来自多个源域的不同预训练检测器，并融合时间信息以生成高质量伪标签用于微调，从而利用了这一特性。我们提出的核密度估计（KDE）框融合方法融合来自多个域的框提议，获得超越最佳源域检测器性能的伪标签。MS3D对域偏移展现出更强的鲁棒性，并在更远距离上生成准确的伪标签，使其特别适合高到低波束域适应及其逆过程。我们的方法在所有评估数据集上均取得了最先进的性能，并且我们证明了预训练源检测器的选择对自训练结果影响极小，从而使MS3D适用于实际应用。