# Topologically Robust 3D Shape Matching via Gradual Deflation and Inflation

## TL;DR

This paper introduces a novel 3D shape matching method that uses gradual deflation and inflation to normalize topology, improving robustness against topological noise in diverse 3D models.

## Contribution

The paper presents a new topology normalization approach for 3D shape matching that outperforms existing methods under high topological noise conditions.

## Key findings

- Outperforms existing methods with increased topological noise
- Effective in normalizing shape topology before matching
- Demonstrated on multiple datasets

## Abstract

Despite being vastly ignored in the literature, coping with topological noise is an issue of increasing importance, especially as a consequence of the increasing number and diversity of 3D polygonal models that are captured by devices of different qualities or synthesized by algorithms of different stabilities. One approach for matching 3D shapes under topological noise is to replace the topology-sensitive geodesic distance with distances that are less sensitive to topological changes. We propose an alternative approach utilising gradual deflation (or inflation) of the shape volume, of which purpose is to bring the pair of shapes to be matched to a \emph{comparable} topology before the search for correspondences. Illustrative experiments using different datasets demonstrate that as the level of topological noise increases, our approach outperforms the other methods in the literature.