We propose an unsupervised learning framework with the pretext task of
finding dense correspondences between point cloud shapes from the same category
based on the cycle-consistency formulation. In order to learn discriminative
pointwise features from point cloud data, we incorporate in the formulation a
regularization term based on Sinkhorn normalization to enhance the learned
pointwise mappings to be as bijective as possible. Besides, a random rigid
transform of the source shape is introduced to form a triplet cycle to improve
the model's robustness against perturbations. Comprehensive experiments
demonstrate that the learned pointwise features through our framework benefits
various point cloud analysis tasks, e.g. partial shape registration and
keypoint transfer. We also show that the learned pointwise features can be
leveraged by supervised methods to improve the part segmentation performance
with either the full training dataset or just a small portion of it.

提出了一种基于无监督学习框架的密集对应点云的预定任务模型，并且加入了基于 Sinkhorn 正则化，提高模型的鲁棒性，通过实验表明通过我们的模型能够学习出对各种点云分析任务有益的点对点的特征信息。