In this study, we introduce a novel local image descriptor, which is very efficient to compute densely. We also present an algorithm to compute dense depth maps from image pairs using designed descriptor. Novel descriptor is based on visual primitives and relations between them, namely coplanarity, cocolority, distance, and angle. Designed feature descriptor covers both geometric and appearance information. The depth map estimation performance is evaluated using the established bad matched pixel metric. An analysis of the feature descriptor employing a parallel programming paradigm is included to develop a possible real-time mode. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA ® GeForce ® GT640 graphic card and Matlab over a PC with Windows 10.

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Dario I. Rosas-Miranda, Volodymyr I. Ponomaryov, and Cesar M. A. Robles-Gonzalez, "An efficient dense descriptor applied to 3D vision implemented on parallel computing," Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 1067007 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 16, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2303667.