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GPF: GMM-Inspired Feature-Preserving Point Set Filtering

Version 2 2024-06-13, 12:55
Version 1 2019-04-09, 12:28
journal contribution
posted on 2024-06-13, 12:55 authored by X Lu, S Wu, H Chen, SK Yeung, W Chen, M Zwicker
Point set filtering, which aims at reconstructing noise-free point sets from their corresponding noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of point set filtering is to preserve geometric features of the underlying geometry while at the same time removing the noise. State-of-the-art point set filtering methods still struggle with this issue: some are not designed to recover sharp features, and others cannot well preserve geometric features, especially fine-scale features. In this paper, we propose a novel approach for robust feature-preserving point set filtering, inspired by the Gaussian Mixture Model (GMM). Taking a noisy point set and its filtered normals as input, our method can robustly reconstruct a high-quality point set which is both noise-free and feature-preserving. Various experiments show that our approach can soundly outperform the selected state-of-the-art methods, in terms of both filtering quality and reconstruction accuracy.

History

Journal

IEEE Transactions on Visualization and Computer Graphics

Volume

24

Pagination

2315-2326

Location

United States

ISSN

1077-2626

eISSN

1941-0506

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

8

Publisher

IEEE COMPUTER SOC