Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis

TL;DR

This paper introduces a diffusion geometry framework that fuses geometric and photometric data for improved non-rigid shape analysis, demonstrating its effectiveness over traditional methods.

Contribution

It proposes a novel diffusion-based approach to combine geometric and photometric information in shape descriptors, enhancing analysis capabilities.

Findings

Data fusion improves shape analysis robustness

Method outperforms pure geometric or photometric approaches

Effective in handling challenging shape variations

Abstract

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.