Inverse Rendering for High-Genus 3D Surface Meshes from Multi-view Images with Persistent Homology Priors

TL;DR

This paper presents a novel inverse rendering method that uses persistent homology priors to accurately reconstruct complex high-genus 3D surfaces from multi-view images, overcoming topological ambiguities.

Contribution

It introduces a topological prior-based approach using persistent homology to improve high-genus 3D reconstruction without neural networks.

Findings

Lower Chamfer Distance compared to state-of-the-art methods

Higher Volume IoU indicating better geometric accuracy

Enhanced robustness against topological failures

Abstract

Reconstructing 3D objects from images is inherently an ill-posed problem due to ambiguities in geometry, appearance, and topology. This paper introduces collaborative inverse rendering with persistent homology priors, a novel strategy that leverages topological constraints to resolve these ambiguities. By incorporating priors that capture critical features such as tunnel loops and handle loops, our approach directly addresses the difficulty of reconstructing high-genus surfaces. The collaboration between photometric consistency from multi-view images and homology-based guidance enables recovery of complex high-genus geometry while circumventing catastrophic failures such as collapsing tunnels or losing high-genus structure. Instead of neural networks, our method relies on gradient-based optimization within a mesh-based inverse rendering framework to highlight the role of topological priors. Experimental results show that incorporating persistent homology priors leads to lower Chamfer Distance (CD) and higher Volume IoU compared to state-of-the-art mesh-based methods, demonstrating improved geometric accuracy and robustness against topological failure.