@inproceedings{
zhang2024reversecomplete,
title={Reverse2Complete: Unpaired Multimodal Point Cloud Completion via Guided Diffusion},
author={Wenxiao Zhang and Hossein Rahmani and Xun Yang and Jun Liu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=fheUSqLh7J}
}
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 GAN-based methods transform 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 an unconditional diffusion model trained on complete data by “hijacking” the generative process. We further augment the diffusion model by introducing two guidance mechanisms to help map 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.
Reverse2Complete: Unpaired Multimodal Point Cloud Completion via Guided Diffusion
Wenxiao Zhang Hossein Rahmani Xun Yang Jun Liu