Low Rank Matrix Approximation for 3D Geometry Filtering. Academic Article

abstract

• 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.

authors

• Schaefer, Scott

published proceedings

• IEEE Trans Vis Comput Graph

author list (cited authors)

• Lu, X., Schaefer, S., Luo, J., Ma, L., & He, Y.

citation count

• 29

complete list of authors

• Lu, Xuequan||Schaefer, Scott||Luo, Jun||Ma, Lizhuang||He, Ying

publication date

• April 2022

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Visualization and Computer Graphics  Journal

keywords

• 3d Geometry Filtering
• Estimation
• Faces
• Geometric Texture Removal
• Geometry
• Mesh Denoising
• Noise Reduction
• Point Cloud Filtering
• Point Upsampling
• Robustness
• Shape
• Surface Reconstruction
• Three-dimensional Displays

Digital Object Identifier (DOI)

• 10.1109/TVCG.2020.3026785

start page

• 1835

end page

• 1847

volume

• 28

issue

• 4

URL

• http://dx.doi.org/10.1109/tvcg.2020.3026785