Denoising point sets via \(L_0\) minimization

DOI10.1016/j.cagd.2015.03.011zbMath1417.68251OpenAlexW2010473040MaRDI QIDQ1632309

Scott Schaefer, Wenping Wang, Yu-Jing Sun

Publication date: 14 December 2018

Published in: Computer Aided Geometric Design (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cagd.2015.03.011

zbMATH Keywords

denoisingpoint set\(L_0\) minimization\(L_0\) sparsity

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (5)

Sparsity-aided variational mesh restoration ⋮ Rolling normal filtering for point clouds ⋮ A unified surface geometric framework for feature-aware denoising, hole filling and context-aware completion ⋮ Sparse regularization with the ℓ0 norm ⋮ Efficient \(L_0\) resampling of point sets

Cites Work

• Nonlinear total variation based noise removal algorithms
• Smooth surface reconstruction via natural neighbour interpolation of distance functions
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Three-dimensional alpha shapes
• The digital TV filter and nonlinear denoising
• Continuous projection for fast L ₁ reconstruction
• Decoupling noise and features via weighted ℓ ₁ -analysis compressed sensing
• Mesh denoising via L ₀ minimization
• Edge-aware point set resampling
• Screened poisson surface reconstruction
• Compressed sensing

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