We introduce a neural implicit framework that exploits the differentiable properties of neural networks and the discrete geometry of point-sampled surfaces to approximate them as the level sets of neural implicit functions. To train a neural implicit function, we propose a loss functional that approximates a signed distance function, and allows terms with high-order derivatives, such as the alignment between the principal directions of curvature, to learn more geometric details. During training, we consider a non-uniform sampling strategy based on the curvatures of the point-sampled surface to prioritize points with more geometric details. This sampling implies faster learning while preserving geometric accuracy when compared with previous approaches. We also use the analytical derivatives of a neural implicit function to estimate the differential measures of the underlying point-sampled surface.
@article{arxiv.2201.09263,
title = {Exploring Differential Geometry in Neural Implicits},
author = {Tiago Novello and Guilherme Schardong and Luiz Schirmer and Vinicius da Silva and Helio Lopes and Luiz Velho},
journal= {arXiv preprint arXiv:2201.09263},
year = {2024}
}