摘要
Unpaired point cloud completion involves filling in missing parts of a point cloud without requiring partial-complete correspondence. Meanwhile, since point cloud completion is an ill-posed problem, there are multiple ways to generate the missing parts. Existing unpaired completion methods usually leverage generative adversarial training by transforming partial shape encoding into a complete one in the low-dimensional latent feature space. However, "mode collapse" often occurs, where only a subset of the shapes is represented in the low-dimensional space, reducing the diversity of the generated shapes. In this paper, we propose a novel unpaired multimodal shape completion approach that directly operates on point coordinate space. We achieve unpaired completion via a single diffusion model trained on complete data by "hijacking" the generative process. We further augment the diffusion model by introducing two guidance mechanisms to facilitate mapping the partial point cloud to the complete one while preserving its original structure. We conduct extensive evaluations of our approach, which show that our method generates shapes that are more diverse and better preserve the original structures compared to alternative methods.
