HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition

We propose a method, HotSpot, for optimizing neural signed distance functions, based on a relation between the solution of a screened Poisson equation and the distance function.Existing losses such as the eikonal loss cannot guarantee the recovered implicit function to be a distance function, even when the implicit function satisfies the eikonal equation almost everywhere.Furthermore, the eikonal loss suffers from stability issues in optimization and the remedies that introduce area or divergence minimization can lead to oversmoothing.We address these challenges by designing a loss function that when minimized can converge to the true distance function, is stable, and naturally penalize large surface area.We provide theoretical analysis and experiments on both challenging 2D and 3D datasets and show that our method provide better surface reconstruction and more accurate distance approximation.