What Is the Range of Surface Reconstructions from a Gradient Field?
By Amit K. Agrawal, Ramesh Raskar, Ramalingam Chellappa. European Conference on Computer Vision, 2006
357 citations recorded by the source corpus.
Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/42d0da659e37124e8d3a722561389099917e15b3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Graphics and Computer-Aided Design
- Brian Curless
- Konrad Schindler
- Michael F. Cohen
- C. Lawrence Zitnick
- Silvano Galliani
- Pravin Bhat
- Matthias Nießner
- Hugues Hoppe
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Andreas Geiger
- Xiaogang Wang
- Jitendra Malik
- Dacheng Tao
- Bernt Schiele
- Trevor Darrell
Citations in the corpus
- Angular Synchronization by Eigenvectors and Semidefinite Programming (2009, unknown): “This may open the way for the eigenvector method based on (100) to be useful for the surface reconstruction problems in computer vision [13,1] and optics [30] in which current methods succeed only in the presence of a limited number of outliers.”
- Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems (2008, background): “[19] explore a wide space of formulations (some of them non-linear) for surface reconstruction that includes the possibility of a data term.”
- Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction (2016, methodology): “It has also been attempted to replace the least-squares error function by more robust norms to improve the robustness to outliers [2].”