Shape from Probability Maps with Image-Adapted Voxelization

Abstract : This paper presents a Bayesian framework for Visual Hull reconstruction from multiple camera views with a 3D sampling scheme based on an irregular 3D grid, which becomes regular once projected onto the available views. The probabilistic framework consists in establishing a foreground probability for each pixel in each view rather than segmenting in order to obtain binary silhouettes of the foreground elements. Next, a Bayesian consistency test labels the occupancy of each image-adapted 3D sample. The proposed method, using image-adapted 3D sampling in the Bayesian framework, is compared to a shape-from-silhouette implementation with image-adapted voxelization, where the input data are binary silhouettes instead of probability maps; we also compare its performance to a state-of-the-art method based on regular 3D sampling with binary silhouettes and SPOT projection test.
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Communication dans un congrès
Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008
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  • HAL Id : inria-00326773, version 1

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Jordi Salvador, Josep R. Casas. Shape from Probability Maps with Image-Adapted Voxelization. Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008. 〈inria-00326773〉

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