Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self-supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly, to enable high-fidelity correspondence label generation, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

翻译：从相距较远的车辆采集的点云配准能够提供驾驶场景全面且精确的三维视图，这对驾驶安全相关应用至关重要，然而现有文献存在昂贵的姿态标签获取成本以及难以泛化至新数据分布的问题。本文提出一种无监督远距离点云配准方法EYOC，该方法无需全局姿态标签即可实时适应新点云分布。EYOC的核心思想是以渐进方式训练特征提取器：在每一轮训练中，使用近点云对训练的特征提取器能够为稍远距离的点云对生成标注，从而实现对远距离点云对的自监督学习。此过程持续进行，直至所获得的特征提取器可用于配准远距离点云。特别地，为生成高保真度的对应关系标签，我们设计了一种有效的空间滤波方案，选取最具代表性的对应关系来配准点云对，并利用对齐后的点云发掘更多正确对应关系。实验表明，EYOC能以更低训练成本达到与最先进监督方法相当的性能，且在泛化至新数据分布方面优于监督方法。