Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.

翻译：现实世界中的传感器通常会产生不完整、不规则且带有噪声的点云，这使得点云补全变得越来越重要。然而，现有的大多数补全方法依赖大型配对数据集进行训练，这一过程劳动密集且耗时。本文提出RaPD，一种新颖的半监督点云补全方法，可减少对配对数据集的需求。RaPD采用两阶段训练方案：在第一阶段从未配对的完整与不完整点云中学习深度语义先验；在第二阶段引入半监督先验蒸馏过程，仅使用少量配对样本训练补全网络。此外，文中还引入自监督补全模块，利用未配对的不完整点云提升性能。在多个数据集上的实验表明，RaPD在同源与异源场景下均优于现有方法。