NeuralTPS: Learning Signed Distance Functions Without Priors From Single Sparse Point Clouds.

Abstract

Surface reconstruction for point clouds is one of the important tasks in 3D computer vision. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors usually do not generalize well to various geometric variations that are unseen during training, especially for extremely sparse point clouds. To resolve this issue, we present a neural network to directly infer SDFs from single sparse point clouds without using signed distance supervision, learned priors or even normals. Our insight here is to learn surface parameterization and SDFs inference in an end-to-end manner. To make up the sparsity, we leverage parameterized surfaces as a coarse surface sampler to provide many coarse surface estimations in training iterations, according to which we mine supervision for our thin plate splines (TPS) based network to infer smooth SDFs in a statistical way. Our method significantly improves the generalization ability and accuracy on unseen point clouds. Our experimental results show our advantages over the state-of-the-art methods in surface reconstruction for sparse point clouds under synthetic datasets and real scans.