Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
On active contour models and balloons
CVGIP: Image Understanding
SIGGRAPH '93 Proceedings of the 20th 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 volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Spacetime-Coherent Geometry Reconstruction from Multiple Video Streams
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation
Computer Vision and Image Understanding
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We present a surface deformation framework for the problem of 3D shape recovery. A spatially smooth and topologically plausible surface mesh representation is constructed via a surface evolution based technique, starting from an initial model. The initial mesh, representing the bounding surface, is refined or simplified where necessary during surface evolution using a set of local mesh transform operations so as to adapt local properties of the object surface. The final mesh obtained at convergence can adequately represent the complex surface details such as bifurcations, protrusions and large visible concavities. The performance of the proposed framework which is in fact very general and applicable to any kind of raw surface data, is demonstrated on the problem of shape reconstruction from silhouettes. Moreover, since the approach we take for surface deformation is Lagrangian, that can track changes in connectivity and geometry of the deformable mesh during surface evolution, the proposed framework can be used to build efficient time-varying representations of dynamic scenes.