Feature Preserving Point Set Surfaces based on Non‐Linear Kernel Regression

By A. Cengiz Öztireli, Gaël Guennebaud, Markus H. Gross. Computer graphics forum (Print), 2009

539 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2cc303759a9a703eb91d4f9faba0666b82dc1ff4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Daniel Cohen-Or
2. Chi-Wing Fu
3. P. Heng
4. Olga Sorkine-Hornung
5. Shihao Wu
6. Yifan Wang
7. Hao Zhang
8. Lequan Yu

In Computer Vision and Pattern Recognition

1. P. Heng
2. Luc Van Gool
3. Daniel Cohen-Or
4. Leonidas J. Guibas
5. Ming-Hsuan Yang
6. Dacheng Tao
7. Chi-Wing Fu
8. Alexei A. Efros

Citations in the corpus

• Voronoi-Based Curvature and Feature Estimation from Point Clouds (2011, background): “[21] define a feature-preserving MLS projection operator by noting that angles between normal vectors, rather than point coordinates can be used to discard points from unrelated parts of a piecewise-smooth surface.”
• Scale Space Meshing of Raw Data Point Sets (2011, methodology): “Variations of the MLS algo-rithm for denoising point sampled surfaces and preserving edges were proposed in [FCOS05], [GTE ∗ 06], [OGG09], [LCOL07], [GG07].”
• Edge-aware point set resampling (2013, methodology): “The results demonstrate that our resampling approach can effectively remove noise, provide reliable normals, preserve sharp features, and facilitate edge-aware reconstruction methods such as RIMLS [Öztireli et al. 2009].”
• Deep points consolidation (2015, methodology): “Representative approaches include anisotropic smoothing [Lange and Polthier 2005], point-sampled cell complexes [Adamson and Alexa 2006], algebraic point set surfaces [Guennebaud and Gross 2007], robust implicit MLS [ ¨ Oztireli et al. 2009] and point set resampling [Huang et al. 2013a].”
• EC-Net: an Edge-aware Point set Consolidation Network (2018, methodology): “To consolidate a point set in an edge-aware manner, some methods detected/sensed the sharp edges and arranged points deliberatively along edges to preserve their sharpness [16,17,18,19].”
• Differentiable surface splatting for point-based geometry processing (2019, methodology): “Bandwidth D and Θ for computing weights in (11) and (12) are set as suggested in previous works [Huang et al. 2009; Öztireli et al. 2009].”
• A Survey of Surface Reconstruction from Point Clouds (2017, unknown): “The Robust Implicit MLS (RIMLS) method [OGG09] discounts outliers by iteratively reweighting points based on their spatial and normal residual errors.”