High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.


Code Image Gray Code Bundle Adjustment Lens Distortion Disparity Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by NSF awards IIS-0917109 and IIS-1320715 to DS.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Middlebury CollegeMiddleburyUSA
  2. 2.German Aerospace CenterOberpfaffenhofenGermany
  3. 3.Reykjavik UniversityReykjavikIceland
  4. 4.LiveRampSan FranciscoUSA

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