We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component). We develop a new differentiable framework based on persistent homology to formulate topological loss terms that enforce the prior of a single 2-manifold object. Our method demonstrates excellent performance in preserving the topology of complex 3D geometries, evident through both visual and empirical comparisons. We supplement this with a theoretical analysis, and provably show that optimizing the loss with stochastic (sub)gradient descent leads to convergence and enables reconstructing shapes with a single connected component. Our approach showcases the integration of differentiable topological data analysis tools for implicit surface reconstruction.
@article{arxiv.2412.18696,
title = {STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology},
author = {Anushrut Jignasu and Ethan Herron and Zhanhong Jiang and Soumik Sarkar and Chinmay Hegde and Baskar Ganapathysubramanian and Aditya Balu and Adarsh Krishnamurthy},
journal= {arXiv preprint arXiv:2412.18696},
year = {2025}
}