Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. In contrast to previous approaches, we present Partial2Complete (P2C), the first self-supervised framework that completes point cloud objects using training samples consisting of only a single incomplete point cloud per object. Specifically, our framework groups incomplete point clouds into local patches as input and predicts masked patches by learning prior information from different partial objects. We also propose Region-Aware Chamfer Distance to regularize shape mismatch without limiting completion capability, and devise the Normal Consistency Constraint to incorporate a local planarity assumption, encouraging the recovered shape surface to be continuous and complete. In this way, P2C no longer needs multiple observations or complete point clouds as ground truth. Instead, structural cues are learned from a category-specific dataset to complete partial point clouds of objects. We demonstrate the effectiveness of our approach on both synthetic ShapeNet data and real-world ScanNet data, showing that P2C produces comparable results to methods trained with complete shapes, and outperforms methods learned with multiple partial observations. Code is available at https://github.com/CuiRuikai/Partial2Complete.

翻译：摘要：点云补全旨在基于局部观测恢复完整形状。现有方法需要完整点云或同一物体的多个局部观测进行学习。与先前方法不同，我们提出Partial2Complete（P2C）——首个利用每个物体仅包含单个不完整点云的训练样本实现自监督点云补全的框架。具体而言，该框架将不完整点云划分为局部块作为输入，通过从不同局部物体中学习先验信息来预测被掩蔽的块。我们同时提出区域感知倒角距离，在限制补全能的前提下规范形状不匹配问题，并设计法向量一致性约束引入局部平面假设，促进恢复形状曲面的连续性与完整性。通过这种方式，P2C不再需要多视角观测或完整点云作为真值，而是从类别特定数据集中学习结构线索以完成物体的局部点云补全。我们在合成ShapeNet数据和真实ScanNet数据上验证了方法的有效性，结果表明P2C能获得与基于完整形状训练的方法相当的结果，且优于基于多个局部观测学习的方法。代码已开源至https://github.com/CuiRuikai/Partial2Complete。