PCCDiff: Point Cloud Completion with Conditional Denoising Diffusion Probabilistic Models

Abstract

Point clouds obtained from laser scanners or other devices often exhibit incompleteness, which poses a challenge for subsequent point cloud processing. Therefore, accurately predicting the complete shape from partial observations has paramount significance. In this paper, we introduce PCCDiff, a probabilistic model inspired by Denoising Diffusion Probabilistic Models (DDPMs), designed for point cloud completion tasks. Our model aims to predict missing parts in incomplete 3D shapes by learning the reverse diffusion process, transforming a 3D Gaussian noise distribution into the desired shape distribution without any structural assumption (e.g., geometric symmetry). Firstly, we design a conditional point cloud completion network that integrates Missing-Transformer and TreeGCN, facilitating the prediction of complete point cloud features. Subsequently, at each step of the diffusion process, the obtained point cloud features serve as condition inputs for the symmetric Diffusion ResUNet. By incorporating these condition features and incomplete point clouds into the diffusion process, PCCDiff demonstrates superior generation performance compared to other methods. Finally, extensive experiments are conducted to demonstrate the effectiveness of our proposed generative model for completing point clouds.