Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud registration methods have recently emerged to circumvent the need for costly pose annotations. However, they fail to establish reliable optimization objectives for unsupervised training, either relying on overly strong geometric assumptions, or suffering from poor-quality pseudo-labels due to inadequate integration of low-level geometric and high-level contextual information. We have observed that in the feature space, latent new inlier correspondences tend to cluster around respective positive anchors that summarize features of existing inliers. Motivated by this observation, we propose a novel unsupervised registration method termed INTEGER to incorporate high-level contextual information for reliable pseudo-label mining. Specifically, we propose the Feature-Geometry Coherence Mining module to dynamically adapt the teacher for each mini-batch of data during training and discover reliable pseudo-labels by considering both high-level feature representations and low-level geometric cues. Furthermore, we propose Anchor-Based Contrastive Learning to facilitate contrastive learning with anchors for a robust feature space. Lastly, we introduce a Mixed-Density Student to learn density-invariant features, addressing challenges related to density variation and low overlap in the outdoor scenario. Extensive experiments on KITTI and nuScenes datasets demonstrate that our INTEGER achieves competitive performance in terms of accuracy and generalizability.

翻译：点云配准作为三维视觉中的基础任务，在户外场景中通过基于学习的方法已取得显著成功。近期出现的无监督户外点云配准方法旨在避免昂贵的位姿标注需求。然而，这些方法未能为无监督训练建立可靠的优化目标：它们要么依赖过强的几何假设，要么因低层几何信息与高层上下文信息整合不足而遭受伪标签质量低下的问题。我们观察到，在特征空间中，潜在的新的内点对应倾向于聚集在各自的正锚点周围，这些锚点总结了现有内点的特征。受此观察启发，我们提出了一种名为INTEGER的新型无监督配准方法，通过整合高层上下文信息来实现可靠的伪标签挖掘。具体而言，我们提出了特征-几何一致性挖掘模块，在训练过程中为每个小批量数据动态调整教师模型，并通过同时考虑高层特征表示与低层几何线索来发现可靠的伪标签。此外，我们提出了基于锚点的对比学习，通过锚点促进对比学习以构建鲁棒的特征空间。最后，我们引入了混合密度学生模型来学习密度不变特征，以应对户外场景中密度变化和低重叠率带来的挑战。在KITTI和nuScenes数据集上的大量实验表明，我们的INTEGER方法在精度和泛化能力方面均取得了具有竞争力的性能。