Abstract
Modern applications of stereo vision, such as advanced driver assistance systems and autonomous vehicles, require highest precision when determining the location and velocity of potential obstacles. Subpixel disparity accuracy in selected image regions is therefore essential. Evaluation benchmarks for stereo correspondence algorithms, such as the popular Middlebury and KITTI frameworks, provide important reference values regarding dense matching performance, but do not sufficiently treat local sub-pixel matching accuracy. In this paper, we explore this important aspect in detail. We present a comprehensive statistical evaluation of selected state-of-the-art stereo matching approaches on an extensive dataset and establish reference values for the precision limits actually achievable in practice. For a carefully calibrated camera setup under real-world imaging conditions, a consistent error limit of 1/10 pixel is determined. We present guidelines on algorithmic choices derived from theory which turn out to be relevant to achieving this limit in practice.
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Alahari, K., Kohli, P., Torr, P.H.S.: Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs. TPAMI 32(10), 1846–1857 (2010)
Baker, S., Matthews, I.: Lucas-Kanade 20 Years On: A Unifying Framework: Part 1. IJCV 56(3), 221–255 (2004)
Elad, M., Teo, P., Hel-Or, Y.: On the Design of Filters for Gradient-Based Motion Estimation. Journal of Mathematical Imaging and Vision 23(3), 345–365 (2005)
Enzweiler, M., Hummel, M., Pfeiffer, D., Franke, U.: Efficient Stixel-Based Object Recognition. In: IV (2012)
Farid, H., Simoncelli, E.P.: Differentiation of Discrete Multidimensional Signals. TIP 13(4), 496–508 (2004)
Förstner, W.: Image Matching. In: Haralick, R.M., Shapiro, L.G. (eds.) Computer and Robot Vision, 2nd edn., ch. 16, pp. 289–372. Addison-Wesley (1993)
Franke, U., Pfeiffer, D., Rabe, C., Knoeppel, C., Enzweiler, M., Stein, F., Herrtwich, R.G.: Making Bertha See. In: ICCV Workshops (2013)
Gehrig, S.K., Eberli, F., Meyer, T.: A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)
Gehrig, S.K., Franke, U.: Improving Stereo Sub-Pixel Accuracy for Long Range Stereo. In: ICCV 2007 Workshops (2007)
Geiger, A., Lenz, P., Urtasun, R.: Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: CVPR, pp. 3354–3361 (2012)
Haller, I., Nedevschi, S.: Design of Interpolation Functions for Subpixel-Accuracy Stereo-Vision Systems. TIP 21(2), 889–898 (2012)
Hirschmüller, H.: Stereo Processing by Semiglobal Matching and Mutual Information. TPAMI 30(2), 328–341 (2008)
Jähne, B.: Digital Image Processing - Concepts, Algorithms, and Scientific Applications, 3rd edn. Springer (1995)
Keys, R.G.: Cubic Convolution Interpolation for Digital Image Processing. ASSP 29(6), 1153–1160 (1981)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. Int. Joint Conf. on Artificial Intel. (1981)
Mester, R.: Motion Estimation Revisited: An Estimation-Theoretic Approach. In: SSIAI (2014)
Nehab, D., Rusinkiewiez, S., Davis, J.: Improved Sub-Pixel Stereo Correspondences Through Symmetric Refinement. In: ICCV, pp. 557–563 (2005)
Pfeiffer, D., Gehrig, S., Schneider, N.: Exploiting the Power of Stereo Confidences. In: CVPR, pp. 297–304 (2013)
Pinggera, P., Franke, U., Mester, R.: Highly Accurate Depth Estimation for Objects at Large Distances. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 21–30. Springer, Heidelberg (2013)
Rabe, C.: Detection of Moving Objects by Spatio-Temporal Motion Analysis. Phd thesis, Christian-Albrechts-Universität zu Kiel (2011)
Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the Limits of Stereo Using Variational Stereo Estimation. In: IV (2012)
Robinson, D., Milanfar, P.: Fundamental Performance Limits in Image Registration. TIP 13(9), 1185–1199 (2004)
Rousseeuw, P.J., Croux, C.: Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association 88(424) (1993)
Sabater, N., Morel, J.M., Almansa, A.: How Accurate Can Block Matches Be in Stereo Vision? SIAM Journal on Imaging Sciences 4(1), 472 (2011)
Sabater, N., Almansa, A., Morel, J.M.: Meaningful Matches in Stereovision. TPAMI 34(5), 930–942 (2012)
Scharr, H.: Optimal Filters for Extended Optical Flow. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 14–29. Springer, Heidelberg (2007)
Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. IJCV 47(1-3), 7–42 (2002)
Shimizu, M., Okutomi, M.: Precise Sub-pixel Estimation on Area-Based Matching. In: ICCV, pp. 90–97 (2001)
Sutton, M.A., Orteu, J.J., Schreier, H.W.: Image Correlation for Shape, Motion and Deformation Measurements. Springer (2009)
Szeliski, R., Scharstein, D.: Sampling the Disparity Space Image. TPAMI 26(3), 419–425 (2004)
Thévenaz, P., Blu, T., Unser, M.: Interpolation Revisited. TMI 19(7) (2000)
Unser, M., Aldroubi, A., Eden, M.: B-Spline Signal Processing. TSP 41(2) (1993)
Vogel, C., Schindler, K., Roth, S.: Piecewise Rigid Scene Flow. In: ICCV (2013)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An Improved Algorithm for TV-L 1 Optical Flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)
Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic Huber-L1 Optical Flow. In: BMVC, pp. 108.1–108.11 (2009)
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Pinggera, P., Pfeiffer, D., Franke, U., Mester, R. (2014). Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_7
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DOI: https://doi.org/10.1007/978-3-319-10605-2_7
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