Point cloud registration, a fundamental task in 3D computer vision, has
remained largely unexplored in cross-source point clouds and unstructured
scenes. The primary challenges arise from noise, outliers, and variations in
scale and density. However, neglected geometric natures of point clouds
restricts the performance of current methods. In this paper, we propose a novel
method termed SPEAL to leverage skeletal representations for effective learning
of intrinsic topologies of point clouds, facilitating robust capture of
geometric intricacy. Specifically, we design the Skeleton Extraction Module to
extract skeleton points and skeletal features in an unsupervised manner, which
is inherently robust to noise and density variances. Then, we propose the
Skeleton-Aware GeoTransformer to encode high-level skeleton-aware features. It
explicitly captures the topological natures and inter-point-cloud skeletal
correlations with the noise-robust and density-invariant skeletal
representations. Next, we introduce the Correspondence Dual-Sampler to
facilitate correspondences by augmenting the correspondence set with skeletal
correspondences. Furthermore, we construct a challenging novel large-scale
cross-source point cloud dataset named KITTI CrossSource for benchmarking
cross-source point cloud registration methods. Extensive quantitative and
qualitative experiments are conducted to demonstrate our approach's superiority
and robustness on both cross-source and same-source datasets. To the best of
our knowledge, our approach is the first to facilitate point cloud registration
with skeletal geometric priors.

本文介绍了一种利用骨架表征进行点云配准的新方法 SPEAL，通过提取点云的骨架特征和拓扑结构，获得点云的几何复杂性，进而提高点云配准的性能。作者还构建了一个名为 KITTI CrossSource 的大规模跨源点云数据集，用于评估该方法在跨源和同源数据集上的优越性和鲁棒性。据我们所知，这是第一个利用骨架几何先验进行点云配准的方法。