Fast surface reconstruction using the level set method

By Hongkai Zhao, Stanley J. Osher, Ronald Fedkiw. Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, 2001

493 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/0cfb6fff98408a89272ca58d425882c19f3695af

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. Yaron Lipman
2. Xavier Bresson
3. Matan Atzmon
4. Tom Goldstein
5. Stanley J. Osher
6. Lior Yariv
7. Yuri Boykov
8. Jonathan Richard Shewchuk

In Computer Graphics and Computer-Aided Design

1. Yaron Lipman
2. Xavier Bresson
3. Lior Yariv
4. Matan Atzmon
5. Tom Goldstein
6. Yuri Boykov
7. Jonathan Richard Shewchuk
8. Chandrajit L. Bajaj

Citations in the corpus

• Global Optimization for Shape Fitting (2007, result): “Our results for sparse or incomplete 3D data from laser scanning and passive multi-view stereo are robust to noise, outliers, missing parts, and varying sampling density.”
• Delaunay Triangulation Based Surface Reconstruction: Ideas and Algorithms (2004, methodology): “There also approaches toward the surface reconstruction problem that are not based on the Delaunay triangulation, e.g., level set methods [ HOF01 ], radial basis function based methods [CBC+01] and moving least squares methods [ABCO+01].”
• Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction (2010, methodology): “A very nice approach that does not suffer from these difficulties is to use an implicit level-set representation [52, 28, 36, 51].”
• Anisotropic diffusion of surfaces and functions on surfaces (2003, methodology): “Recently level set methods were also used in surface fairing and surface reconstruction (see [5], [6], [9], [34], [56], [60], [61]).”
• Local level set method in high dimension and codimension (2004, background): “Its successful applications include multiphase flows [17,19], surface reconstructions [6,7], and image processing [14,22].”
• SAL: Sign Agnostic Learning of Shapes From Raw Data (2019, methodology): “[43] use the level-set method to fit an implicit surface to an unoriented point cloud by minimizing a loss penalizing distance of the surface to the point cloud achieving a sort of minimal area surface interpolating the points.”
• Interpolating and approximating implicit surfaces from polygon soup (2004, methodology): “, 2002] and [Zhao et al., 2001] have used level-set methods for fitting surfaces to point clouds.”
• A quasi-dense approach to surface reconstruction from uncalibrated images (2005, methodology): “In [ 65 ], a convection model is also proposed to ignore the regularization term, , to”
• Implicit Geometric Regularization for Learning Shapes (2020, methodology): “Their work introduced a loss function inspired by the level set method (Osher et al., 2004) based surface reconstruction techniques (Zhao et al., 2000; 2001), operating directly on level sets of RGVF.”
• Space-time isosurface evolution for temporally coherent 3D reconstruction (2004, result): “The geometry reconstructed by this scheme is significantly better than results obtained by space-carving approaches that do not enforce temporal coherence.”