“Towards a Stochastic Depth maps Estimation for Textureless and Quite Specular Surfaces” by Saouli and Babahenini

  • ©Abdelhak Saouli and Mohamed Chaouki Babahenini

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Entry Number: 72

Title:

    Towards a Stochastic Depth maps Estimation for Textureless and Quite Specular Surfaces

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Abstract:


    The human brain is constantly solving enormous and challenging optimization problems in vision. Due to the formidable metaheuristics engine our brain equipped with, in addition to the widespread associative inputs from all other senses that act as the perfect initial guesses for a heuristic algorithm, the produced solutions are guaranteed to be optimal. By the same token, we address the problem of computing the depth and normal maps of a given scene under a natural but unknown illumination utilizing particle swarm optimization (PSO) to maximize a sophisticated photo-consistency function. For each output pixel, the swarm is initialized with good guesses starting with SIFT features as well as the optimal solution (depth, normal) found previously during the optimization. This leads to significantly better accuracy and robustness to textureless or quite specular surfaces.

References:


    • Yasutaka Furukawa and Jean Ponce. 2010. Accurate, Dense, and Robust Multiview Stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 8 (2010), 1362–1376. https: //doi.org/10.1109/TPAMI.2009.161 
    • Fabian Langguth, Kalyan Sunkavalli, Sunil Hadap, and Michael Goesele. 2016. ShadingAware Multi-view Stereo. In Computer Vision – ECCV 2016 – 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III. 469–485. https://doi.org/10.1007/978-3-319-46487-9_29 
    • Steven M. Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 17-22 June 2006, New York, NY, USA. 519–528. https://doi.org/10.1109/CVPR.2006.19 
    • Enliang Zheng, Enrique Dunn, Vladimir Jojic, and Jan-Michael Frahm. 2014. PatchMatch Based Joint View Selection and Depthmap Estimation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014. 1510–1517. https://doi.org/10.1109/CVPR.2014.196.

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