Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Regularizing Flows for Constrained Matrix-Valued Images
Journal of Mathematical Imaging and Vision
Generalized Gradients: Priors on Minimization Flows
International Journal of Computer Vision
Rectangular multi-chart geometry images
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
International Journal of Computer Vision
Regularization of mappings between implicit manifolds of arbitrary dimension and codimension
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
A Super-Resolution Framework for High-Accuracy Multiview Reconstruction
International Journal of Computer Vision
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A novel framework for solving variational problems and partial differential equations for scalar and vector-valued data defined on surfaces is introduced in this paper. The key idea is to implicitly represent the surface as the level set of a higher dimensional function, and solve the surface equations in a fixed Cartesian coordinate system using this new embedding function. The equations are then both intrinsic to the surface and defined in the embedding space. This approach thereby eliminates the need for performing complicated and not-accurate computations on triangulated surfaces, as it is commonly done in the literature. We describe the framework and present examples in computer graphics and image processing applications, including texture synthesis, flow field visualization, as well as image and vector field intrinsic regularization for data defined on 3D surfaces.