@conference {DBLP:conf/isvc/GussefeldHK16,
	title = {Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets},
	booktitle = {Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}},
	year = {2016},
	abstract = {Optical flow ground truth generated by computer graphics has many advantages. For example, we can systematically vary scene parameters to understand algorithm sensitivities. But is synthetic ground truth realistic enough? Appropriate material models have been established as one of the major challenges for the creation of synthetic datasets: previous research has shown that highly sophisticated reflectance field acquisition methods yield results, which various optical flow methods cannot distinguish from real scenes. However, such methods are costly both in acquisition and rendering time and thus infeasible for large datasets. In this paper we find the simplest reflectance models (RM) for different groups of materials which still provide sufficient accuracy for optical flow performance analysis. It turns out that a spatially varying Phong RM is sufficient for simple materials. Normal estimation combined with Anisotropic RM can handle even very complex materials.},
	doi = {10.1007/978-3-319-50835-1_8},
	author = {G{\"u}ssefeld, Burkhard and Katrin Honauer and Daniel Kondermann}
}
@conference {Honauer-etal-2017-ICCV,
	title = {A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields},
	booktitle = {Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	address = {Cham},
	abstract = {In computer vision communities such as stereo, optical flow, or visual tracking, commonly accepted and widely used benchmarks have enabled objective comparison and boosted scientific progress. In  the  emergent  light  field  community,  a  comparable  benchmark  and evaluation methodology is still missing. The performance of newly proposed methods is often demonstrated qualitatively on a handful of images, making quantitative comparison and targeted progress very difficult. To overcome these difficulties, we propose a novel light field benchmark. We provide 24 carefully designed synthetic, densely sampled 4D light fields with highly accurate disparity ground truth. We thoroughly evaluate four state-of-the-art light field algorithms and one multi-view stereo algorithm using existing and novel error measures. This consolidated state-of-the art  may serve as a baseline to stimulate and guide further scientific progress. We publish the benchmark website http://www.lightfield-analysis.net , an evaluation toolkit, and our rendering setup to encourage submissions of both algorithms and further datasets.},
	isbn = {978-3-319-54186-0},
	doi = {10.1007/978-3-319-54187-7_2},
	author = {Katrin Honauer and Ole Johannsen and Daniel Kondermann and Bastian Goldl{\"u}cke},
	editor = {Lai, Shang-Hong}
}
@conference {Kondermann_2016_CVPR_Workshops,
	title = {The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	year = {2016},
	month = {June},
	abstract = { 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.
},
	author = {Daniel Kondermann and Nair, Rahul and Katrin Honauer and Karsten Krispin and Jonas Andrulis and Alexander Brock and G{\"u}ssefeld, Burkhard and Mohsen Rahimimoghaddam and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@conference {Honauer-etal-2015-ICCV,
	title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	year = {2015},
	month = {December},
	abstract = {@InProceedings{Honauer_2015_ICCV,
author = {Honauer, Katrin and Maier-Hein, Lena and Kondermann, Daniel},
title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} },
	author = {Katrin Honauer and Lena Maier-Hein and Daniel Kondermann}
}
@inbook {Kondermann-etal-2015-ACCV,
	title = {Stereo Ground Truth with Error Bars},
	booktitle = {Computer Vision {\textendash} ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V},
	year = {2015},
	pages = {595{\textendash}610},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-319-16814-2},
	doi = {10.1007/978-3-319-16814-2_39},
	url = {http://dx.doi.org/10.1007/978-3-319-16814-2_39},
	author = {Daniel Kondermann and Nair, Rahul and Stephan Meister and Wolfgang Mischler and G{\"u}ssefeld, Burkhard and Katrin Honauer and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
