<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Stephan Meister</style></author><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Sabine Hofmann</style></author><author><style face="normal" font="default" size="100%">Brenner, Claus</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo Ground Truth with Error Bars</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-16814-2_39</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">595–610</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-16814-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Creating stereo ground truth based on real images is a measurement task. Measurements are never perfectly accurate: the depth at each pixel follows an error distribution. A common way to estimate the quality of measurements are error bars. In this paper we describe a methodology to add error bars to images of previously scanned static scenes. The main challenge for stereo ground truth error estimates based on such data is the nonlinear matching of 2D images to 3D points. Our method uses 2D feature quality, 3D point and calibration accuracy as well as covariance matrices of bundle adjustments. We sample the reference data error which is the 3D depth distribution of each point projected into 3D image space. The disparity distribution at each pixel location is then estimated by projecting samples of the reference data error on the 2D image plane. An analytical Gaussian error propagation is used to validate the results. As proof of concept, we created ground truth of an image sequence with 100 frames. Results show that disparity accuracies well below one pixel can be achieved, albeit with much large errors at depth discontinuities mainly caused by uncertain estimates of the camera location.</style></abstract></record></records></xml>