STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology

[Submitted on 24 Dec 2024]

View a PDF of the paper titled STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology, by Anushrut Jignasu and 7 other authors

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Abstract: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.