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» Supershape Recovery from 3D Data Sets
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ICVGIP
2004
14 years 4 days ago
Markerless Motion Capture from Monocular Videos
We present a method to determine the 3D spatial locations of joints of a human body from a monocular video sequence of a Bharatanatyam dance. The proposed method uses domain speci...
Vishal Mamania, Appu Shaji, Sharat Chandran
VMV
2008
124views Visualization» more  VMV 2008»
14 years 5 days ago
Augmented Compression for Server-Side Rendering
In this work we recall attention to problems that arise in a client-server setting with server-side rendering and propose a practical method for accelerated high-quality render-st...
Fabian Giesen, Ruwen Schnabel, Reinhard Klein
ECCV
2006
Springer
14 years 2 months ago
A Batch Algorithm for Implicit Non-rigid Shape and Motion Recovery
The recovery of 3D shape and camera motion for non-rigid scenes from single-camera video footage is a very important problem in computer vision. The low-rank shape model consists ...
Adrien Bartoli, Søren I. Olsen
CVPR
2008
IEEE
15 years 23 days ago
Efficient object shape recovery via slicing planes
Recovering the three-dimensional (3D) object shape lies as an unresolved and active research topic on the crosssection of computer vision, photogrammetry and bioinformatics. Altho...
Po-Lun Lai, Alper Yilmaz
ACCV
2007
Springer
14 years 5 months ago
Evolving Measurement Regions for Depth from Defocus
Depth from defocus (DFD) is a 3D recovery method based on estimating the amount of defocus induced by finite lens apertures. Given two images with different camera settings, the ...
Scott McCloskey, Michael S. Langer, Kaleem Siddiqi