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。

无监督深度概率方法用于部分点云配准

Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration

We present a self-supervised learning (SSL) method suitable for semi-global
tasks such as object detection and semantic segmentation. We enforce local
consistency between self-learned features, representing corresponding image
locations of transformed versions of the same image, by minimizing a
pixel-level local contrastive (LC) loss during training. LC-loss can be added
to existing self-supervised learning methods with minimal overhead. We evaluate
our SSL approach on two downstream tasks -- object detection and semantic
segmentation, using COCO, PASCAL VOC, and CityScapes datasets. Our method
outperforms the existing state-of-the-art SSL approaches by 1.9% on COCO object
detection, 1.4% on PASCAL VOC detection, and 0.6% on CityScapes segmentation.

提出了一种自监督学习方法，适用于对象检测和语义分割等半全局任务，通过在训练期间最小化像素级的本地对比损失（LC-loss），在自学习特征中强制实现对变换后的同一图像的相应图像位置之间的本地一致性，可将 LC-loss 添加到现有的自监督学习方法中，并在 COCO、PASCAL VOC 和 CityScapes 数据集上评估 SSL 方法，结果比现有的先进自监督学习方法在 COCO 对象检测、PASCAL VOC 检测和 CityScapes 分割等任务上分别提高了 1.9％，1.4％和 0.6％。