2014 Stereo datasets with ground truth

These 33 datasets were created by Nera Nesic, Porter Westling, Xi Wang, York Kitajima, Greg Krathwohl, and Daniel Scharstein at Middlebury College during 2011-2013, and refined with Heiko Hirschmüller at the DLR Germany during 2014. A detailed description of the acquisition process can be found in our GCPR 2014 paper [5]. 20 of the datasets are used in the new Middlebury Stereo Evaluation (10 each for training and test sets). Except for the 10 test datasets, we provide links to directories containing the full-size views and disparity maps. Shown are the left views at 5% resolution; moving the mouse over the images shows the right views. (More details below.)

  10 evaluation test sets (GT hidden)










  10 evaluation training sets with GT
Adirondack: perf, imp

Jadeplant: perf, imp

Motorcycle: perf, imp

Piano: perf, imp

Pipes: perf, imp

Playroom: perf, imp

Playtable: perf, imp

Recycle: perf, imp

Shelves: perf, imp

Vintage: perf, imp

  13 additional datasets with GT
Backpack: perf, imp

Bicycle1: perf, imp

Cable: perf, imp

Classroom1: perf, imp

Couch: perf, imp

Flowers: perf, imp

Mask: perf, imp

Shopvac: perf, imp

Sticks: perf, imp

Storage: perf, imp

Sword1: perf, imp

Sword2: perf, imp

Umbrella: perf, imp

Dataset description

Each dataset consists of 2 views taken under several different illuminations and exposures. The files are organized as follows:
SCENE-{perfect,imperfect}/     -- each scene comes with perfect and imperfect calibration (see paper)
  ambient/                     -- directory of all input views under ambient lighting
    L{1,2,...}/                -- different lighting conditions
      im0e{0,1,2,...}.png      -- left view under different exposures
      im1e{0,1,2,...}.png      -- right view under different exposures
  calib.txt                    -- calibration information
  im{0,1}.png                  -- default left and right view
  im1E.png                     -- default right view under different exposure
  im1L.png                     -- default right view with different lighting
  disp{0,1}.pfm                -- left and right GT disparities
  disp{0,1}-n.png              -- left and right GT number of samples (* perfect only)
  disp{0,1}-sd.pfm             -- left and right GT sample standard deviations (* perfect only)
  disp{0,1}y.pfm               -- left and right GT y-disparities (* imperfect only)
Zip files containing the above files (except for the "ambient" subdirectories) for each scene can be downloaded here.

Calibration file format

Here is a sample calib.txt file for one of the full-size training image pairs:
cam0=[3997.684 0 1176.728; 0 3997.684 1011.728; 0 0 1]
cam1=[3997.684 0 1307.839; 0 3997.684 1011.728; 0 0 1]
The calibration files provided with the test image pairs used in the stereo evaluation only contain the first 7 lines, up to the "ndisp" parameter.


cam0,1:        camera matrices for the rectified views, in the form [f 0 cx; 0 f cy; 0 0 1], where
  f:           focal length in pixels
  cx, cy:      principal point  (note that cx differs between view 0 and 1)

doffs:         x-difference of principal points, doffs = cx1 - cx0

baseline:      camera baseline in mm

width, height: image size

ndisp:         a conservative bound on the number of disparity levels;
               the stereo algorithm MAY utilize this bound and search from d = 0 .. ndisp-1

isint:         whether the GT disparites only have integer precision (true for the older datasets;
               in this case submitted floating-point disparities are rounded to ints before evaluating)

vmin, vmax:    a tight bound on minimum and maximum disparities, used for color visualization;
               the stereo algorithm MAY NOT utilize this information

dyavg, dymax:  average and maximum absolute y-disparities, providing an indication of
               the calibration error present in the imperfect datasets.
To convert from the floating-point disparity value d [pixels] in the .pfm file to depth Z [mm] the following equation can be used:
Z = baseline * f / (d + doffs)
Note that the image viewer "sv" and mesh viewer "plyv" provided by our software cvkit can read the calib.txt files and provide this conversion automatically when viewing .pfm disparity maps as 3D meshes.

Last modified: July 9 2015 by Daniel Scharstein