Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Computer Vision and Image Understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Level set surface editing operators
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Voxel Carving for Specular Surfaces
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Variational Analysis of Shape from Specularities using Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Surface Reconstruction from the Projection of Points, Curves and Contours
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Local Shape from Mirror Reflections
International Journal of Computer Vision
PDE based shape from specularities
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A geometric formulation of gradient descent for variational problems with moving surfaces
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
IEEE Transactions on Image Processing
Arranging and interpolating sparse unorganized feature points with geodesic circular arc
IEEE Transactions on Image Processing
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Many visual cues for surface reconstruction from known views are sparse in nature, e.g., specularities, surface silhouettes, and salient features in an otherwise textureless region. Often, these cues are the only information available to an observer. To allow these constraints to be used either in conjunction with dense constraints such as pixel-wise similarity, or alone, we formulate such constraints in a variational framework. We propose a sparse variational constraint in the level set framework, enforcing a surface to pass through a specific point, and a sparse variational constraint on the surface normal along the observed viewing direction, as is the nature of, e.g., specularities. These constraints are capable of reconstructing surfaces from extremely sparse data. The approach has been applied and validated on the shape from specularities problem.