Data-driven geometry-recovering mesh denoising

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

Research output: Journal PublicationsJournal Article (refereed)peer-review

38 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)133-142
Number of pages10
JournalCAD Computer Aided Design
Early online date14 May 2019
Publication statusPublished - Sept 2019
Externally publishedYes


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


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