Transformer Augmented Multi-Resolution Hash Encoding in Diffusion Model for 3D Point Cloud Denoising

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.