|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
A PROBABILISTIC FRAMEWORK FOR SPACE CARVING
A. Broadhurst, T. Drummond and R. Cipolla
This paper introduces a new probabilistic framework for Space Carving. In this framework each voxel is assigned a probability, which is computed by comparing the likelihoods for the voxel existing and not existing.
This new framework avoids many of the difficulties associated with the original Space Carving algorithm. Specifically, it does not need a global threshold parameter, and it guarantees that no holes will be carved in the model. This paper also proposes that a voxel-based thick texture is a realistic and efficient representation for scenes which contain dominant planes. The algorithm is tested using both real and synthetic data, and both qualitative and quantitative results are presented.
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