Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction. (arXiv:2109.04398v3 [cs.CV] UPDATED)

Surface reconstruction is very challenging when the input point clouds,
particularly real scans, are noisy and lack normals. Observing that the
Multilayer Perceptron (MLP) and the implicit moving least-square function
(IMLS) provide a dual representation of the underlying surface, we introduce
Neural-IMLS, a novel approach that directly learns the noise-resistant signed
distance function (SDF) from unoriented raw point clouds in a self-supervised
fashion. We use the IMLS to regularize the distance values reported by the MLP
while using the MLP …

arxiv least moving network squares