| CARVIEW |
Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering
ICCV 2025
Abstract
Estimating normals for noisy point clouds is a persistent challenge in 3D geometry processing, particularly for end-to-end oriented normal estimation. Existing methods generally address relatively clean data and rely on supervised priors to fit local surfaces within specific neighborhoods. In this paper, we propose a novel approach for learning normals from noisy point clouds through local gradient-aware surface filtering. Our method projects noisy points onto the underlying surface by utilizing normals and distances derived from an implicit function constrained by local gradients. We start by introducing a distance measurement operator for global surface fitting on noisy data, which integrates projected distances along normals. Following this, we develop an implicit field-based filtering approach for surface point construction, adding projection constraints on these points during filtering. To address issues of over-smoothing and gradient degradation, we further incorporate local gradient consistency constraints, as well as local gradient orientation and aggregation. Comprehensive experiments on normal estimation, surface reconstruction, and point cloud denoising demonstrate the state-of-the-art performance of our method.
Method
Overview of our method. It can be used for different tasks such as surface reconstruction, point cloud denoising, and normal estimation without the need for training labels.
Results
Normal RMSE
Visual comparison of oriented normals on two point clouds with complex geometry. Colors indicate normal errors.
Surface Reconstruction
Visual comparison of reconstructed surfaces. As the noise increases (from low to high), our method becomes more advantageous.
BibTeX
@inproceedings{li2025LGSF,
title={Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering},
author={Li, Qing and Feng, Huifang and Gong, Xun and Liu, Yu-Shen},
booktitle={International Conference on Computer Vision (ICCV)},
year={2025}
}