Research on Shape Mapping of 3D Mesh Models based on Hidden Markov Random Field and EM Algorithm

TL;DR

This paper introduces a novel shape mapping algorithm for 3D mesh models using Hidden Markov Random Field and EM algorithm, improving the consistency and accuracy of shape correspondence.

Contribution

It presents a new shape mapping method based on HMRF and EM, incorporating hidden state variables to enhance matching consistency between 3D shapes.

Findings

Significant improvement in shape mapping accuracy

Enhanced edge data consistency between adjacent blocks

Effective handling of shape correspondence challenges

Abstract

How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing HMRF. This algorithm provides a new theory and method to ensure the consistency of the edge data of adjacent blocks, and the experimental results show that the algorithm in this paper has a great improvement on the shape mapping of 3D mesh models.