Low Rank Matrix Approximation for 3D Geometry Filtering

We propose a robust normal estimation method for both point clouds and meshes using a low rank matrix approximation algorithm. First, we compute a local isotropic structure for each point and find its similar, non-local structures that we organize into a matrix. We then show that a low rank matrix a...

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Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 28; no. 4; pp. 1835 - 1847
Main Authors Lu, Xuequan, Schaefer, Scott, Luo, Jun, Ma, Lizhuang, He, Ying
Format Journal Article
LanguageEnglish
Published United States IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
3D geometry filtering
Algorithms
Approximation
Estimation
Faces
Filtration
geometric texture removal
Geometry
Mathematical analysis
mesh denoising
Noise reduction
point cloud filtering
point upsampling
Robustness
Shape
Surface layers
surface reconstruction
Three-dimensional displays
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Summary:We propose a robust normal estimation method for both point clouds and meshes using a low rank matrix approximation algorithm. First, we compute a local isotropic structure for each point and find its similar, non-local structures that we organize into a matrix. We then show that a low rank matrix approximation algorithm can robustly estimate normals for both point clouds and meshes. Furthermore, we provide a new filtering method for point cloud data to smooth the position data to fit the estimated normals. We show the applications of our method to point cloud filtering, point set upsampling, surface reconstruction, mesh denoising, and geometric texture removal. Our experiments show that our method generally achieves better results than existing methods.
Bibliography:ObjectType-Article-1
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ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2020.3026785