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.
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Notes
- 1.
Although most of the following works comprise additional datasets next to stereo data, we only focus on the latter.
- 2.
Horizontal vertical focal lengths \((f_x, f_y)\), principle point \((c_x, c_y)\).
- 3.
- 4.
Cross correlation window: 21 \(\times \) 21. Search neighborhood 21 \(\times \) 21.
- 5.
Screenshots and usage videos of the tools can be found in the supplemental material.
- 6.
Based on the maximum deviation of the angle-axis vector.
- 7.
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Acknowledgments
We thank Wolfgang Niehsen and his Team at Robert Bosch GmbH, Computer Vision Research Lab, Hildesheim, for supplying the test car, camera mount and tons of input regarding meaningful content of the scenes we recorded. We further thank Jens Taupadel, Jakob Knauer and Moritz Wandsleb at Universität Hannover for acquiring and processing the scans. Finally, we thank our lab members Karsten Krispin, Alexandro Sanchez-Bach, Ekaterina Melnik for their assistance in data processing, Florian Becker and Frank Lenzen for helpful discussions as well as AEON Verlag&Studio GmbH for the organization of all helpers and facilities.
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Kondermann, D. et al. (2015). Stereo Ground Truth with Error Bars. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_39
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