Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme to extract density-invariant geometric features to register distant outdoor LiDAR point clouds. We mark through theoretical analysis and experiments that, contrastive positives should be independent and identically distributed (i.i.d.), in order to train densityinvariant feature extractors. We propose upon the conclusion a simple yet effective training scheme to force the feature of multiple point clouds in the same spatial location (referred to as positive groups) to be similar, which naturally avoids the sampling bias introduced by a pair of point clouds to conform with the i.i.d. principle. The resulting fully-convolutional feature extractor is more powerful and density-invariant than state-of-the-art methods, improving the registration recall of distant scenarios on KITTI and nuScenes benchmarks by 40.9% and 26.9%, respectively. The code will be open-sourced.

翻译：远距离室外LiDAR点云的配准对于扩展协同自动驾驶车辆的3D视觉至关重要，但由于重叠区域小且观测点密度差异巨大，这一任务颇具挑战性。本文提出分组对比学习（GCL）方案，以提取密度不变性几何特征，用于配准远距离室外LiDAR点云。通过理论分析与实验，我们指出对比正样本应满足独立同分布（i.i.d.）条件，从而训练出密度不变性特征提取器。基于此结论，我们提出一种简单而有效的训练方案，强制同一空间位置多个点云（称为正样本组）的特征保持相似，这自然避免了因一对点云引入的采样偏差，符合i.i.d.原理。由此产生的全卷积特征提取器比现有主流方法更强大且更具密度不变性，在KITTI和nuScenes基准上，远距离场景的配准召回率分别提升了40.9%和26.9%。代码将开源。