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Disparity map estimation in a stereo pair of images with cost volume filtering
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pmonasse/stereo-guided-filter
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Stereo Disparity through Cost Aggregation with Guided Filter Pauline Tan, <[email protected]>, CMLA, ENS Cachan, France Pascal Monasse, <[email protected]>, LIGM, Univ. Paris-Est, France Version 1.2 released on 2023/03/22 Future releases and updates: https://github.com/pmonasse/stereo-guided-filter - Requirements CMake for the build system (http://www.cmake.org/) - Build cd stereo-guided-filter_1.0 mkdir Build && cd Build cmake -D CMAKE_BUILD_TYPE:string=Release .. make - Run Usage: ./stereoGuidedFilter [options] im1.png im2.png dmin dmax Options (default values in parentheses) Cost-volume filtering parameters: -R radius: radius of the guided filter (9) -A alpha: value of alpha (0.9) -E epsilon: regularization parameter (6.5025) -C tau1: max for color difference (7) -G tau2: max for gradient difference (2) Occlusion detection: -o tolDiffDisp: tolerance for left-right disp. diff. (0) Densification: -O sense: fill occlusion, sense='r':right,'l':left -r radius: radius of the weighted median filter (19) -c sigmac: value of sigma_color (25.5) -s sigmas: value of sigma_space (9) -a grayMin: value of gray for min disparity (255) -b grayMax: value of gray for max disparity (0) The parameter 'sense' used in densification is the direction of camera motion: - from left to right (value 'r'), common for Middlebury pairs - from right to left (value 'l') - Output image files disparity.png: disparity map after cost-volume filtering disparity_occlusion.png: after left-right check disparity_occlusion_filled.png: simple densification disparity_occlusion_filled_smoothed.png: final densification with median filter - Test ./stereoGuidedFilter -O r ../data/tsukuba0.png ../data/tsukuba1.png -15 0 Compare resulting image files with those in folder data.
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Disparity map estimation in a stereo pair of images with cost volume filtering
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