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.

提出了一种新的自我训练管道 MS3D，用于无监督领域适应的 3D 目标检测，其使用不同的预训练探测器结合时间信息生成高质量的伪标签进行微调。使用我们提出的核密度估算（KDE）盒融合方法来融合来自多个域的盒子提议，以获得超过最佳源域探测器性能的伪标签。MS3D 表现出更大的领域位移鲁棒性，并能够在较大距离上产生准确的伪标签，因此非常适合从高到低波束领域适应和反之。该方法在所有评估数据集上都取得了最先进的性能，而且我们证明了预先训练源探测器的选择对自我训练结果影响较小，因此 MS3D 适用于实际应用。