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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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https://hal.inria.fr/inria-00326773
Contributor : Peter Sturm <>
Submitted on : Sunday, October 5, 2008 - 2:47:27 PM
Last modification on : Monday, November 16, 2020 - 3:56:17 PM
Long-term archiving on: : Monday, October 8, 2012 - 2:00:21 PM

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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, Andrea Cavallaro and Hamid Aghajan, Oct 2008, Marseille, France. ⟨inria-00326773⟩

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