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This paper introduces Electrostatics-Inspired Surface Reconstruction (EISR), a novel method for 3D surface reconstruction that represents shapes as solutions to Poisson's equation. By drawing an analogy to electrostatics and utilizing Green's functions, the method derives a closed-form parametric expression for the implicit field. The key result is improved reconstruction of high-frequency details compared to existing SDF-based methods, even with limited shape priors, by leveraging the superposition principle of Poisson's equation solutions.
Ditch Eikonal PDEs: this surface reconstruction method encodes shapes as solutions to Poisson's equation, unlocking better high-frequency detail with fewer priors.
Implicit shape representation, such as SDFs, is a popular approach to recover the surface of a 3D shape as the level sets of a scalar field. Several methods approximate SDFs using machine learning strategies that exploit the knowledge that SDFs are solutions of the Eikonal partial differential equation (PDEs). In this work, we present a novel approach to surface reconstruction by encoding it as a solution to a proxy PDE, namely Poisson's equation. Then, we explore the connection between Poisson's equation and physics, e.g., the electrostatic potential due to a positive charge density. We employ Green's functions to obtain a closed-form parametric expression for the PDE's solution, and leverage the linearity of our proxy PDE to find the target shape's implicit field as a superposition of solutions. Our method shows improved results in approximating high-frequency details, even with a small number of shape priors.