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Wang H, Zhang N, Creput JC, et al. Parallel Structured Mesh Generation with Disparity Maps by GPU Implementation. IEEE Trans Vis Comput Graph. 2015;21(9):1045-57doi: 10.1109/TVCG.2015.2413775.
Wang, H., Zhang, N., Creput, J. C., Moreau, J., & Ruichek, Y. (2015). Parallel Structured Mesh Generation with Disparity Maps by GPU Implementation. IEEE transactions on visualization and computer graphics, 21(9), 1045-57. https://doi.org/10.1109/TVCG.2015.2413775
Wang, Hongjian, et al. "Parallel Structured Mesh Generation with Disparity Maps by GPU Implementation." IEEE transactions on visualization and computer graphics vol. 21,9 (2015): 1045-57. doi: https://doi.org/10.1109/TVCG.2015.2413775
Wang H, Zhang N, Creput JC, Moreau J, Ruichek Y. Parallel Structured Mesh Generation with Disparity Maps by GPU Implementation. IEEE Trans Vis Comput Graph. 2015 Sep;21(9):1045-57. doi: 10.1109/TVCG.2015.2413775. PMID: 26357286.
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IEEE Trans Vis Comput Graph. 2015 Sep;21(9):1045-57. doi: 10.1109/TVCG.2015.2413775.

Parallel Structured Mesh Generation with Disparity Maps by GPU Implementation.

IEEE transactions on visualization and computer graphics

Hongjian Wang, Naiyu Zhang, Jean-Charles Creput, Julien Moreau, Yassine Ruichek

PMID: 26357286 DOI: 10.1109/TVCG.2015.2413775

Abstract

The goal of structured mesh is to generate a compressed representation of the 3D surface, where near objects are provided with more details than objects far from the camera, according to the disparity map. The solution is based on the Kohonens Self-Organizing Map algorithm for the benefits of its ability to generate a topological map according to a probability distribution and its potential to be a natural massive parallel algorithm. The disparity map, which stands for a density distribution that reflects the proximity of objects to the camera, is partitioned into an appropriate number of cell units, in such a way that each cell is associated to a processing unit and responsible of a certain area of the plane. The advantage of the proposed model is that it is decentralized and based on data decomposition. The required processing units and memory are with linearly increasing relationship to the problem size. Experimental results show that our GPU implementation is able to provide near real-time performance with small size disparity maps and the running time increases in a linear way with a very weak increasing coefficient. The proposed method is suitable to deal with large scale problems in a massively parallel way.

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