Transformer augmented multi-resolution hash encoding in diffusion model for 3D point cloud denoising

Abstract

Denoising 3D point cloud strives to remove noise from noisy data. Existing methods address the problem by estimating point-wise displacement from the point feature or by learning the distribution of noise. In this paper, we propose to embed the point cloud through a novel multi-resolution hash encoding, and utilize the embedding to learn an optimum transport plan between noisy and corresponding clean point cloud via transformer encoder and a shared-MLP based decoder. The multi-resolution hash encoding uses hierarchical hash-based representations to efficiently capture geometric details at multiple resolutions. Hence, it enables removal of noise while encoding global-to-local structural details. The transformer encoder further improves the model’s ability to learn long-range dependencies and contextual relationships, facilitating improved denoising performance. The optimum transport plan is devised by simulating a denoising diffusion probabilistic model through Schrödinger bridge problem. The proposed method advances state-of-the-art methods through extensive experiments and offers new insights into the synergy between hash encoding, and transformer architectures in the diffusion framework.