论文标题
sf-uda $^{3D} $：基于激光雷达的3D对象检测的无源无监督域改编
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
论文作者
论文摘要
仅基于LiDAR Point Clouds的3D对象探测器在现代街道视图基准上持有最新的。但是，由于域移位，基于激光雷达的探测器在整个域之间概括了很大的概括。实际上，在LIDAR的情况下，域移位不仅是由于环境和对象外观的变化所致，因为来自RGB摄像机的视觉数据，还与点云的几何形状有关（例如点密度变化）。本文提出了Sf-uda $^{3D} $，这是第一个无源的无监督域适应性（SF-UDA）框架，用于域，以适应最先进的POINTRCNN 3D检测器，以使我们没有注释（未经审核）的目标域（我们都没有图像），也不持有映像的源代码（来源）。 sf-uda $^{3d} $在这两个方面都是新颖的。我们的方法基于伪通量，可逆的规模转化和运动相干性。 Sf-uda $^{3D} $基于功能对齐和最新的3D对象检测方法的先前域适应技术，这些技术还使用了很少的目标注释或目标注释统计。在两个大规模数据集（即Kitti和Nuscenes）上进行了广泛的实验证明了这一点。
3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern street-view benchmarks. However, LiDAR-based detectors poorly generalize across domains due to domain shift. In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e.g., point density variations). This paper proposes SF-UDA$^{3D}$, the first Source-Free Unsupervised Domain Adaptation (SF-UDA) framework to domain-adapt the state-of-the-art PointRCNN 3D detector to target domains for which we have no annotations (unsupervised), neither we hold images nor annotations of the source domain (source-free). SF-UDA$^{3D}$ is novel on both aspects. Our approach is based on pseudo-annotations, reversible scale-transformations and motion coherency. SF-UDA$^{3D}$ outperforms both previous domain adaptation techniques based on features alignment and state-of-the-art 3D object detection methods which additionally use few-shot target annotations or target annotation statistics. This is demonstrated by extensive experiments on two large-scale datasets, i.e., KITTI and nuScenes.
