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This repository provides a MATLAB implementation of a procedure to precisely interpolate corresponding image points and sample stereo-image patches from stereo-images with co-registered ground-truth distance measurements.
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============ = Overview = ============ This repository provides a MATLAB implementation of a procedure to precisely sample binocular corresponding points from stereo images with co-registered ground-truth distance measurements. Code is provided for the binocular corresponding-point sampling and interpolation procedure described in the paper: Arvind Iyer & Johannes Burge (submitted) "The Effect of Depth Variation on Disparity Tasks in Natural Scenes" Journal of Vision This repository also includes two example stereo-images with co-registered distance measurements (see below) ================== = Using the code = ================== Step 1: Save the code to a folder and add it with subfolders to your Matlab path Step 2: Open the tutorial script LRSIstereoImageSamplingDemo.m and evaluate each section. ======================= = Principal functions = ======================= 1) LRSIcorrespondingPoint.m: Finds corresponding points 2) LRSIcorrespondingPointVet.m: Screens for bad points 3) LRSIcropStereoPatch.m: Interpolates and crops stereo-patches 4) LRSIcorrespondingPointAddDisparity.m: Adds fixation disparity 5) vergenceFromRangeXYZ.m: Vergence demand from range data 6) vergenceFromCorrespondingPoints.m: Vergence demand from corresponding points ==================== = Helper Functions = ==================== Functions from the open-source "geom3D" library authored by David Legland are utilized by the functions in this repository. The library can be accessed at: https://www.mathworks.com/matlabcentral/fileexchange/24484-geom3d ========================= = Function Descriptions = ========================= All Matlab functions in this repository contain detailed descriptions of the input and output parameters. ======== = Data = ======== This code depends on Luminance Range Stereo-Images (LRSI)- luminance images with co-registered distance measurements- from the dataset described in: Johannes Burge, Brian McCann, & Wilson Geisler (2016) "Estimating 3D Tilt from Local Image Cues in Natural Scenes" Journal of Vision, 16(2), doi:10.1167/16.13.2 If you use this data for your research project, please cite the above paper. The full dataset of all 99 luminance-range-stereo-images can be downloaded at http://natural-scenes.cps.utexas.edu/db.shtml under the section heading "Stereo Image and Range Data Collection”. 1) LRSItestImg*.mat: Example Luminance Range Stereo Images (2) + Limg: left-eye (LE) luminance image + Rimg: right-eye (RE) luminance image + Lrng: LE range (m) of each imaged surface points in scene + Rrng: RE range (m) of each imaged surface points in scene + Lxyz: LE cartesian coordinates of surface points in scene + Rxyz: RE cartesian coordinates of surface points in scene (All distances are in meters) 2) LRSIprojPlaneAnchorEye.m Function that provides… + LppXm: projection plane x-coords in LE coordinate system + LppYm: projection plane y-coords in LE coordinate system + RppXm: projection plane x-coords in RE coordinate system + RppYm: projection plane y-coords in RE coordinate system + CppXm: projection plane x-coords in CE coordinate system + CppYm: projection plane y-coords in CE coordinate system + CppZm: projection plane z-coords + IPDm : Inter-camera distance during data acquisition (All distances are in meters)