作者：Guofeng Mei Hao Tang Xiaoshui Huang Weijie Wang Juan Liu Jian Zhang Luc Van Gool Qiang Wu
深度点云配准方法面临部分重叠和依赖标记数据的挑战。为了解决这些问题，我们提出了UDPReg，这是一种用于具有部分重叠的点云的无监督深度概率配准框架。具体来说，我们首先采用网络从点云中学习高斯混合模型（GMM）的后验概率分布。为了处理局部点云配准，我们应用Sinkhorn算法来预测在GMM的混合权重约束下的分布水平对应关系。为了实现无监督学习，我们设计了三种基于分布一致性的损失：自一致性、交叉一致性和局部对比。自一致性损失是通过鼓励欧几里得和特征空间中的GMM共享相同的后验分布来公式化的。交叉一致性损失源于属于同一集群的两个部分重叠的点云的点共享集群cen这一事实
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.
论文链接：http://arxiv.org/pdf/2303.13290v1
更多计算机论文：http://cspaper.cn/