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Cellular Gpu Model for Structured Mesh Generation and Its Application to the Stereo-Matching Disparity Map

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In Proc. of IEEE International Symposium on Multimedia (ISM'2013), Anaheim, California, USA, 2013.
DOI: 10.1109/ISM.2013.18.
This paper presents a cellular GPU model for structured mesh generation according to an input stereo-matching disparity map. Here, the disparity map stands for a density distribution that reflects the proximity of objects to the camera in 3D space. The meshing process consists in covering such data density distribution with a topological structured hexagonal grid that adapts itself and deforms according to the density values. The goal is to generate a compressed mesh where the nearest objects are provided with more details than objects which are far from the camera. The solution we propose is based on the Kohonen's Self-Organizing Map learning algorithm for the benefit of its ability to generate a topological map according to a probability distribution and its ability to be a natural massive parallel algorithm. We propose a GPU parallel model and its implantation of the SOM standard algorithm, and present experiments on a set of standard stereo- matching disparity map benchmarks.
image matching, mesh generation, probability, self- organising feature maps, stereo image processing
Publication Category:
International conference with proceedings
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