Abstract
Variational methods that evolve surfaces according to PDEs have been quite successful for solving the multiview stereo shape reconstruction problem since [1]. However just like every other algorithm that tackles this problem, their running time is quite high (from dozens of minutes to several hours). Fortunately graphics hardware has shown a great potential for speeding up many low-level computer vision tasks. In this paper, we present the analysis of the different bottlenecks of the original implementation of [2] and show how to efficently port it to GPUs using well-known GPGPU techniques. We finally present some results and discuss the improvements. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Labatut, P., Keriven, R., & Pons, J. P. (2006). A GPU implementation of level set multiview stereo. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3994 LNCS-IV, pp. 212–219). Springer Verlag. https://doi.org/10.1007/11758549_33
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