Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement

TL;DR

This paper presents a novel 3D mesh interest point detection method using geometric measures and sparse refinement, improving accuracy over existing techniques by effectively identifying key points on 3D surfaces.

Contribution

The proposed GMSR method introduces a new geometric response function and an $$ norm optimization for refined interest point detection on 3D meshes, outperforming current state-of-the-art methods.

Findings

Outperforms several state-of-the-art 3D interest point detectors

Effective in distinguishing interest points from edges and flat areas

Utilizes multi-scale geometric measures for robust detection

Abstract

Three dimensional (3D) interest point detection plays a fundamental role in 3D computer vision and graphics. In this paper, we introduce a new method for detecting mesh interest points based on geometric measures and sparse refinement (GMSR). The key point of our approach is to calculate the 3D interest point response function using two intuitive and effective geometric properties of the local surface on a 3D mesh model, namely Euclidean distances between the neighborhood vertices to the tangent plane of a vertex and the angles of normal vectors of them. The response function is defined in multi-scale space and can be utilized to effectively distinguish 3D interest points from edges and flat areas. Those points with local maximal 3D interest point response value are selected as the candidates of 3D interest points. Finally, we utilize an $\ell_0$ norm based optimization method to refine the candidates of 3D interest points by constraining its quality and quantity. Numerical experiments demonstrate that our proposed GMSR based 3D interest point detector outperforms current several state-of-the-art methods for different kinds of 3D mesh models.