<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</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%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Karsten Krispin</style></author><author><style face="normal" font="default" size="100%">Jonas Andrulis</style></author><author><style face="normal" font="default" size="100%">Alexander Brock</style></author><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Mohsen Rahimimoghaddam</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%">The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving</style></title><secondary-title><style face="normal" font="default" size="100%">The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%"> Recent advances in autonomous driving require more and more highly realistic reference data, even for difficult situations such as low light and bad weather. We present a new stereo and optical flow dataset to complement existing benchmarks. It was specifically designed to be representative for urban autonomous driving, including realistic, systematically varied radiometric and geometric challenges which were previously unavailable. The accuracy of the ground truth is evaluated based on Monte Carlo simulations yielding full, per-pixel distributions. Interquartile ranges are used as uncertainty measure to create binary masks for arbitrary accuracy thresholds and show that we achieved uncertainties better than those reported for comparable outdoor benchmarks. Binary masks for all dynamically moving regions are supplied with estimated stereo and flow values. An initial public benchmark dataset of 55 manually selected sequences between 19 and 100 frames long are made available in a dedicated website featuring interactive tools for database search, visualization, comparison and benchmarking.
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