In this study, we address a gap in existing unsupervised domain adaptation
approaches on LiDAR-based 3D object detection, which have predominantly
concentrated on adapting between established, high-density autonomous driving
datasets. We focus on sparser point clouds, capturing scenarios from different
perspectives: not just from vehicles on the road but also from mobile robots on
sidewalks, which encounter significantly different environmental conditions and
sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation
for 3D Object Detection (UADA3D). UADA3D does not depend on pre-trained source
models or teacher-student architectures. Instead, it uses an adversarial
approach to directly learn domain-invariant features. We demonstrate its
efficacy in various adaptation scenarios, showing significant improvements in
both self-driving car and mobile robot domains. Our code is open-source and
will be available soon.

我们在这项研究中填补现有无监督领域自适应方法的空白，这些方法主要集中于适应已经建立的高密度自动驾驶数据集。我们专注于稀疏的点云，从不同的角度捕捉场景，不仅来自道路上的车辆，还来自人行道上的移动机器人，遇到显著不同的环境条件和传感器配置。我们引入了无监督对抗领域自适应三维物体检测（UADA3D），不依赖于预训练源模型和师生架构，而是使用对抗性方法直接学习域不变特征。我们在各种适应场景中证明了其功效，在自动驾驶汽车和移动机器人领域都取得了显著的改进。我们的代码是开源的，即将推出。