Data-driven geometry-recovering mesh denoising

Jun WANG, Jin HUANG, Fu Lee WANG, Mingqiang WEI, Haoran XIE, Jing QIN

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1 Citation (Scopus)

Abstract

Depth cameras and 3D scanners significantly simplify the procedure of geometric modeling. 3D surfaces have become more widespread, leading to a great demand for noise removal with the expectation of the minimal disturbance of mesh geometry. We propose a novel two-step data-driven mesh denoising approach. The first step removes noise by learning normal variations from noisy models to their ground-truth counterparts. Unlike existing denoising methods, we present the second step to recover the mesh geometry lost in the first step. The second step understands the commonly used filters by learning the mapping from filtered models to their ground-truth counterparts. In addition, (1) to handle noise with large variations, we model normal estimation as a low-rank matrix recovery problem in similar-patch collaboration before the first-step learning; (2) to recover the real geometry of a denoised mesh, we reversely filter the denoised mesh to obtain more geometry cues before the second-step learning. The detailed quantitative and qualitative results on various data demonstrate that, our two-step learning algorithm competes favorably with the state-of-the-art methods in terms of mesh geometry preservation and noise-robustness. Copyright © 2019 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)133-142
JournalComputer-Aided Design
Volume114
Early online dateMay 2019
DOIs
Publication statusPublished - Sep 2019

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Geometry
Learning algorithms
Cameras
Recovery

Citation

Wang, J., Huang, J., Wang, F. L., Wei, M., Xie, H., & Qin, J. (2019). Data-driven geometry-recovering mesh denoising. Computer-Aided Design, 114, 133-142. doi: 10.1016/j.cad.2019.05.027

Keywords

  • Mesh denoising
  • Geometry-recovering
  • Low-rank matrix recovery
  • Reverse filter
  • Normal variation learning