Deep point cloud registration methods face challenges to partial overlaps and
rely on labeled data. To address these issues, we propose UDPReg, an
unsupervised deep probabilistic registration framework for point clouds with
partial overlaps. Specifically, we first adopt a network to learn posterior
probability distributions of Gaussian mixture models (GMMs) from point clouds.
To handle partial point cloud registration, we apply the Sinkhorn algorithm to
predict the distribution-level correspondences under the constraint of the
mixing weights of GMMs. To enable unsupervised learning, we design three
distribution consistency-based losses: self-consistency, cross-consistency, and
local contrastive. The self-consistency loss is formulated by encouraging GMMs
in Euclidean and feature spaces to share identical posterior distributions. The
cross-consistency loss derives from the fact that the points of two partially
overlapping point clouds belonging to the same clusters share the cluster
centroids. The cross-consistency loss allows the network to flexibly learn a
transformation-invariant posterior distribution of two aligned point clouds.
The local contrastive loss facilitates the network to extract discriminative
local features. Our UDPReg achieves competitive performance on the
3DMatch/3DLoMatch and ModelNet/ModelLoNet benchmarks.

提出了一种名为 UDPReg 的无监督深度概率点云配准框架，使用 GMM 的后验概率分布、Sinkhorn 算法和三种分布一致性损失函数来解决点云配准中的部分重叠和标记数据问题，并且在多项基准测试中实现了竞争性的性能表现。