3D Geometric salient patterns analysis on 3D meshes

TL;DR

This paper introduces a scale-aware geometric texture analysis method for 3D meshes that clusters texels based on similarity, aiding semantic annotation and demonstrating robustness to mesh simplification and noise.

Contribution

It presents a novel, efficient approach for geometric texture analysis on 3D meshes that is scale-aware and capable of semantic annotation of salient texels.

Findings

Effective clustering of texels in real-world and synthetic meshes

Robustness to mesh simplification and surface noise

Practical application in semantic annotation

Abstract

Pattern analysis is a wide domain that has wide applicability in many fields. In fact, texture analysis is one of those fields, since the texture is defined as a set of repetitive or quasi-repetitive patterns. Despite its importance in analyzing 3D meshes, geometric texture analysis is less studied by geometry processing community. This paper presents a new efficient approach for geometric texture analysis on 3D triangular meshes. The proposed method is a scale-aware approach that takes as input a 3D mesh and a user-scale. It provides, as a result, a similarity-based clustering of texels in meaningful classes. Experimental results of the proposed algorithm are presented for both real-world and synthetic meshes within various textures. Furthermore, the efficiency of the proposed approach was experimentally demonstrated under mesh simplification and noise addition on the mesh surface. In this paper, we present a practical application for semantic annotation of 3D geometric salient texels.