NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function

TL;DR

NeuralGF introduces an unsupervised neural network approach to estimate oriented normals from 3D point clouds, eliminating the need for ground truth normals and improving robustness to noise and outliers.

Contribution

It proposes a novel neural gradient function paradigm for unsupervised normal estimation directly from point clouds, avoiding reliance on synthetic or supervised data.

Findings

Outperforms existing methods on benchmark datasets

Robust to noise, outliers, and density variations

Capable of both oriented and unoriented normal estimation

Abstract

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision. However, normal supervision in benchmarks comes from synthetic shapes and is usually not available from real scans, thereby limiting the learned priors of these methods. In addition, normal orientation consistency across shapes remains difficult to achieve without a separate post-processing procedure. To resolve these issues, we propose a novel method for estimating oriented normals directly from point clouds without using ground truth normals as supervision. We achieve this by introducing a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds and yield unit-norm gradients at the points. Specifically, we introduce loss functions to facilitate query points to iteratively reach the moving targets and aggregate onto the approximated surface, thereby learning a global surface representation of the data. Meanwhile, we incorporate gradients into the surface approximation to measure the minimum signed deviation of queries, resulting in a consistent gradient field associated with the surface. These techniques lead to our deep unsupervised oriented normal estimator that is robust to noise, outliers and density variations. Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks than the latest methods. The source code and pre-trained model are publicly available at https://github.com/LeoQLi/NeuralGF.