Point cloud completion aims to recover the complete 3D shape of an object from partial observations. While approaches relying on synthetic shape priors achieved promising results in this domain, their applicability and generalizability to real-world data are still limited. To tackle this problem, we propose a self-supervised framework, namely RealDiff, that formulates point cloud completion as a conditional generation problem directly on real-world measurements. To better deal with noisy observations without resorting to training on synthetic data, we leverage additional geometric cues. Specifically, RealDiff simulates a diffusion process at the missing object parts while conditioning the generation on the partial input to address the multimodal nature of the task. We further regularize the training by matching object silhouettes and depth maps, predicted by our method, with the externally estimated ones. Experimental results show that our method consistently outperforms state-of-the-art methods in real-world point cloud completion.

本研究针对现有方法在真实世界数据上适用性不足的问题，提出了一种自监督框架RealDiff，将点云补全视为基于现实测量的条件生成问题。通过模拟缺失物体部件的扩散过程，并结合额外的几何线索，RealDiff在处理噪声观测数据时表现出色，实验结果表明该方法在现实世界的点云补全中显著优于当前最先进的方法。

RealDiff：使用自监督扩散模型进行现实世界的3D形状补全