Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometrically deformed models: a method for extracting closed geometric models form volume data
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Free-form shape design using triangulated surfaces
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel FEM-based dynamic framework for subdivision surfaces
Proceedings of the fifth ACM symposium on Solid modeling and applications
Skin: a constructive approach to modeling free-form shapes
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Optimal surface reconstruction from planar contours
Communications of the ACM
Dynamic Catmull-Clark Subdivision Surfaces
IEEE Transactions on Visualization and Computer Graphics
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically adaptable snakes
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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This paper presents a novel, powerful reconstruction algorithm that can recover correct shape geometry as well as its unknown topology from arbitrarily complicated volumetric datasets. The algorithm starts from a simple seed model (of genus zero) that can be initialized automatically without user intervention. The deformable behavior of the model is then governed by a locally defined objective function associated with each vertex of the model. Through the numerical computation of function optimization, the algorithm can adaptively subdivide the model geometry, automatically detect self-collision of the model, properly modify its topology (because of the occurrence of self-collision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global subdivision.