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2019 | OriginalPaper | Chapter

Architectures for Stereo Vision

Authors : Christian Banz, Nicolai Behmann, Holger Blume, Peter Pirsch

Published in: Handbook of Signal Processing Systems

Publisher: Springer International Publishing

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Abstract

Stereo vision is an elementary problem for many computer vision tasks. It has been widely studied under the following two aspects, increasing the quality of the results and accelerating the computational processes. This chapter provides theoretic background on stereo vision systems and discusses architectures and implementations for real-time applications. In particular, the computationally intensive part, the stereo matching, is discussed using one of the leading algorithms, the semi-global matching (SGM) as an example. For this algorithm two implementations are presented in detail on two of the most relevant platforms for real-time image processing today: Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). Thus, the major differences in designing parallelization techniques for extremely different image processing platforms can be illustrated.

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Ambrosch, K., Kubinger, W.: Accurate hardware-based stereo vision. Computer Vision and Image Understanding, Elsevier 114, 1303–1316 (2010)CrossRef

Arias-Estrada, M., Xicotencatl, J., Brebner, G., Woods, R.: Multiple stereo matching using an extended architecture. Proc. Field-Programmable Logic and Applications 2147, 203–212 (2001)CrossRef

Bailey, D.G.: Design for embedded image processing on FPGAs. John Wiley & Sons, Singapore (2011)CrossRef

Banz, C., Blume, H., Pirsch, P.: Real-time semi-global matching disparity estimation on the GPU. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 514–521 (2011)

Banz, C., Blume, H., Pirsch, P.: Evaluation of penalty functions for SGM cost aggregation. Intl. Archives of Photogrammetry and Remote Sensing (2012)

Banz, C., Dolar, C., Cholewa, F., Blume, H.: Instruction set extension for high throughput disparity estimation in stereo image processing. Proc. IEEE Intl. Conf. Architectures and Processors Application-Specific Systems pp. 169–175 (2011)

Banz, C., Hesselbarth, S., Flatt, H., Blume, H., Pirsch, P.: Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation. Proc. IEEE Intl. Conf. Embedded Computer Systems: Architectures, Modeling, and Simulation pp. 93–101 (2010)

Banz, C., Hesselbarth, S., Flatt, H., Blume, H., Pirsch, P.: Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation. Trans. High-Performance Embedded Architectures and Compilers, Springer (2012)

Belhumeur, P.N.: A bayesian approach to binocular steropsis. Intl. Journal of Computer Vision 19(3), 237–260 (1996)CrossRef

10.

Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. Pattern Analysis and Machine Intelligence 20(4), 401–406 (1998)CrossRef

11.

Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. Proc. IEEE Intl. Conf. Computer Vision 1, 377–384 (1999)

12.

Bradski, G., Kaehler, A.: Learning OpenCV, 1 edn. O’Reilly, Sebastopol (2008)

13.

Brunton, A., Chang, S., Roth, G.: Belief propagation on the GPU for stereo vision. Proc. Canadian Conf. Computer and Robot Vision p. 76 (2006)

14.

Chang, N., Lin, T.M., Tasi, T.H., Tseng, Y.C., Chang, T.S.: Real-time DSP implementation on local stereo matching. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 2090–2093 (2007)

15.

Chang, N., Tasi, T.H., Hsu, B., Chen, Y., Chang, T.S.: Algorithm and architecture of disparity estimation with mini-census adaptive support weight. IEEE Trans. Circuits and Systems for Video Technology 20(6), 792–805 (2010)CrossRef

16.

Chu, P.P.: RTL hardware design using VHDL: Coding for efficiency, portability, and scalability. Wiley-Interscience, Hoboken and N.J (2006)CrossRef

17.

Cornells, N., van Gool, L.: Real-time connectivity constrained depth map computation using programmable graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, 1099–1104 (2005)

18.

Darabiha, A., MacLean, W., Rose, J.: Reconfigurable hardware implementation of a phase-correlation stereo algorithm. Machine Vision and Applications, Springer 17, 116–132 (2006)CrossRef

19.

Diaz, J., Ros, E., Carrillo, R., Prieto, A.: Real-time system for high-image resolution disparity estimation. IEEE Trans. Image Processing 16(1), 280–285 (2007)MathSciNetCrossRef

20.

Ernst, I., Hirschmüller, H.: Mutual information based semi-global stereo matching on the GPU. Proc. Intl. Symp. Visual Computing 5358, 228–239 (2008)

21.

Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2008)

22.

Faugeras, O., Viéville, T., Theron, E., Vuillemin, J., Hotz, B., Zhang, Z., Moll, L., Bertin, P., Mathieu, H., Fua, P., Berry, G., Proy, C.: Real-time correlation-based stereo: Algorithm, implementations and applications (1993)

23.

Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Intl. Journal of Computer Vision, Springer 70, 41–54 (2006)

24.

Forstmann, S., Kanou, Y., Jun, O., Thuering, S., Schmitt, A.: Real-time stereo by using dynamic programming. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 29 (2004)

25.

Gehrig, S., Rabe, C.: Real-time semi-global matching on the CPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 85–92 (2010)

26.

Gehrig, S.K., Eberli, F., Meyer, T.: A real-time low-power stereo vision engine using semi-global matching. Proc. Intl. Conf. Computer Vision Systems 5815, 134–143 (2009)CrossRef

27.

Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2012)

28.

Georgoulas, C., Andreadis, I.: A real-time fuzzy hardware structure for disparity map computation. Journal of Real-Time Image Processing, Springer 6(4), 257–273 (2011)CrossRef

29.

Gibson, J., Marques, O.: Stereo depth with a unified architecture GPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 1–6 (2008)

30.

Gong, M.: Real-time joint disparity and disparity flow estimation on programmable graphics hardware. Computer Vision and Image Understanding, Elsevier 113(1), 90–100 (2009)CrossRef

31.

Gong, M., Yang, R., Wang, L., Gong Mingwei: A performance study on different cost aggregation approaches used in real-time stereo matching. Intl. Journal of Computer Vision, Springer 75, 283–296 (2007)

32.

Gong, M., Yang, Y.H.: Near real-time reliable stereo matching using programmable graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, 924–931 (2005)

33.

Güney, F., Geiger, A.: Displets: Resolving stereo ambiguities using object knowledge. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2015)

34.

Haller, I., Nedevschi, S.: GPU optimization of the SGM stereo algorithm. Proc. IEEE Intl. Conf. Intelligent Computer Communication and Processing pp. 197–202 (2010)

35.

Haller, I., Nedevschi, S.: Design of interpolation functions for subpixel-accuracy stereo-vision systems. IEEE Trans. Image Processing 21(2), 889–898 (2012)MathSciNetCrossRef

36.

Harris: Optimizing parallel reduction in CUDA (2007). Whitepaper included in Nvidia Cuda SDK 4.0

37.

Hartley, R.I., Zisserman, A.: Multiple view geometry in computer vision, 2. ed., 7. print. edn. Cambridge Univ. Press, Cambridge (2010)

38.

Hennessy, J.L., Patterson, D.A.: Computer architecture: A quantitative approach, 5 edn. Morgan Kaufmann, San Francisco and Calif and Oxford (2011)MATH

39.

Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. Proc. IEEE Conf. Computer Vision and Pattern Recognition 2, 807–814 (2005)

40.

Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Analysis and Machine Intelligence 30(2), 328–341 (2008)CrossRef

41.

Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2007)

42.

Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Analysis and Machine Intelligence 31(9), 1582–1599 (2009)CrossRef

43.

Hosni, A., Bleyer, M., Rhemann, C., Gelautz, M., Rother, C.: Real-time local stereo matching using guided image filtering. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 1–6 (2011)

44.

Hosseini, F., Fijany, A., Safari, S., Fontaine, J.: Fast implementation of dense stereo vision algorithms on a highly parallel SIMD architecture. Journal of Real-Time Image Processing, Springer pp. 1–15 (2011)

45.

Humenberger, M., Zinner, C., Kubinger, W.: Performance evaluation of a census-based stereo matching algorithm on embedded and multi-core hardware. Proc. Intl. Symp. Image and Signal Processing and Analysis pp. 388–393 (2009)

46.

Ivanchenko, V., Shen, H., Coughlan, J.: Elevation-based MRF stereo implemented in real-time on a GPU. Workshop Applications of Computer Vision pp. 1–8 (2009)

47.

Jiangbo, L., Rogmans, S., Lafruit, G., Catthoor, F.: Real-time stereo correspondence using a truncated separable laplacian kernel approximation on graphics hardware. Proc. IEEE Intl. Conf. Multimedia and Expo pp. 1946–1949 (2007)

48.

Jiangbo, L., Zhang, K., Lafruit, G., Catthoor, F.: Real-time stereo matching: a cross-based local approach. Proc. IEEE Intl. Conf. Acoustics, Speech and Signal Processing pp. 733–736 (2009)

49.

Jin, S., Cho, J., Pham, X.D., Lee, K.M., Park, S.K., Kim, M., Jeon, J.W.: FPGA design and implementation of a real-time stereo vision system. IEEE Trans. Circuits and Systems for Video Technology 20(1), 15–26 (2010)CrossRef

50.

Kalarot, R., Morris, J.: Comparison of FPGA and GPU implementations of real-time stereo vision. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 9–15 (2010)

51.

Kalarot, R., Morris, J., Gimel’farb, G.: Performance analysis of multi-resolution symmetric dynamic programming stereo on GPU. Proc. Intl. Conf. Image and Vision Computing New Zealand pp. 1–7 (2010)

52.

Kanade, T., Yoshida, A., Oda, K., Kano, H., Tanaka, M.: A stereo machine for video-rate dense depth mapping and its new applications. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 196–202 (1996)

53.

Ke, Z., Jiangbo, L., Qiong, Y., Lafruit, G., Lauwereins, R., van Gool, L.: Real-time and accurate stereo: A scalable approach with bitwise fast voting on CUDA. IEEE Trans. Circuits and Systems for Video Technology 21(7), 867–878 (2011)CrossRef

54.

Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A.: End-to-end learning of geometry and context for deep stereo regression. arXiv preprint arXiv:1703.04309 (2017)

55.

Kim, J., Kolmogorov, V., Zabih, R.: Visual correspondence using energy minimization and mutual information. Proc. IEEE Intl. Conf. Computer Vision pp. 1033–1040 (2003)

56.

Konolige, K.: Small vision systems: Hardware and implementation. Proc. Intl. Symp. Robotic Research (1997)

57.

Kung, M., Au, O., Wong, P., Chun, H.L.: Block based parallel motion estimation using programmable graphics hardware. Proc. Intl. Conf. Audio, Language and Image Processing pp. 599–603 (2008)

58.

Lee, S.H., Yi, J., Kim, J.S.: Real-time stereo vision on a reconfigurable system. Proc. Intl. Conf. Embedded Computer Systems: Architectures, Modeling, and Simulation Workshops 3553, 299–307 (2005)

59.

Liang, C., Cheng, C., Lai, Y., Chen, L., Chen, H.: Hardware-efficient belief propagation. IEEE Trans. Circuits and Systems for Video Technology 21(5), 525–537 (2011)CrossRef

60.

Liang, W., Miao, L., Minglun, G., Ruigang, Y., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 798–805 (2006)

61.

Liang, W., Mingwei, G., Minglun, G., Ruigang, Y.: How far can we go with local optimization in real-time stereo matching. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 129–136 (2006)

62.

Liu, J., Xu, Y., Klette, R., Chen, H., Vaudrey, T.: Disparity Map Computation on a Cell Processor (2009)

63.

Luo, W., Schwing, A., Urtasun, R.: Efficient deep learning for stereo matching. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2016)

64.

van der Mark, W., Gavrila, D.: Real-time dense stereo for intelligent vehicles. IEEE Trans. Intelligent Transportation Systems 7(1), 38–50 (2006)CrossRef

65.

Masrani, D., MacLean, W.: A real-time large disparity range stereo-system using FPGAs. Proc. Intl. Conf. Computer Vision Systems p. 13 (2006)

66.

Mattoccia, S., Viti, M., Ries, F.: Near real-time fast bilateral stereo on the GPU. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop pp. 136–143 (2011)

67.

Mayer, N., Ilg, E., Häusser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2016)

68.

Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 467–474 (2011)

69.

Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2015)

70.

Menze, M., Heipke, C., Geiger, A.: Joint 3D estimation of vehicles and scene flow. ISPRS Workshop on Image Sequence Analysis (2015)

71.

Minglun, G., Ruigang, Y.: Image-gradient-guided real-time stereo on graphics hardware. Proc. Intl Conf. 3D Digital Imaging and Modeling pp. 548–555 (2005)

72.

Miyajima, Y., Maruyama, T.: A real-time stereo vision system with FPGA. Proc. Intl. Conf. Field Programmable Logic And Application 2778, 448–457 (2003)CrossRef

73.

Morris, J., Jawed, K., Gimel’farb, G., Khan, T.: Breaking the ‘Ton’: Achieving 1% depth accuracy from stereo in real time. Proc. Intl. Conf. Image and Vision Computing New Zealand pp. 142–147 (2009)

74.

Mühlmann, K., Maier, D., Hesser, J., Manner, R.: Calculating dense disparity maps from color stereo images, an efficient implementation. Intl. Journal of Computer Vision, Springer 47(1–3), 79–88 (2002)

75.

Pantilie, C., Nedevschi, S.: SORT-SGM: Subpixel optimized real-time semiglobal matching for intelligent vehicles. IEEE Trans. Vehicular Technology 61(3), 1032–1042 (2012)CrossRef

76.

Park, S., Jeong, H.: Real-time stereo vision FPGA chip with low error rate. Proc. Intl. Conf. Multimedia and Ubiquitous Engineering pp. 751–756 (2007)

77.

Paya Vaya, G., Martin Langerwerf, J., Banz, C., Giesemann, F., Pirsch, P., Blume, H.: VLIW architecture optimization for an efficient computation of stereoscopic video applications. Proc. Intl. Conf. Green Circuits and Systems pp. 457–462 (2010)

78.

Paya-Vaya, G., Martin-Langerwerf, J., Pirsch, P.: A multi-shared register file structure for VLIW processors. Journal of Signal Processing Systems, Springer 58(2), 215–231 (2010)CrossRef

79.

Perez, J., Sanchez, P., Martinez, M.: High memory throughput FPGA architecture for high-definition Belief-Propagation stereo matching. Proc. Intl. Conf. Signals, Circuits and Systems pp. 1–6 (2009)

80.

Pirsch, P.: Architectures for digital signal processing. John Wiley & Sons, Inc., Chichester (2008)

81.

Pock, T., Schoenemann, T., Graber, G., Bischof, H., Cremers, D.: A convex formulation of continuous multi-label problems. Proc. European Conf. on Computer Vision 5304, 792–805 (2008)

82.

Podlozhnyuk, V.: Image Convolution with CUDA (2007). Whitepaper included in Nvidia Cuda SDK 4.0

83.

Ranft, B., Schoenwald, T., Kitt, B.: Parallel matching-based estimation - a case study on three different hardware architectures. Proc. IEEE Intelligent Vehicles Symposium pp. 1060–1067 (2011)

84.

Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2016)

85.

Roy, S., Cox, I.: A maximum-flow formulation of the n-camera stereo correspondence problem. Proc. of Intl. Conf. on Computer Vision (1998)

86.

Ruigang, Y., Pollefeys, M.: Multi-resolution real-time stereo on commodity graphics hardware. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, I–211–I–217 (2003)

87.

Ruigang, Y., Welch, G., Bishop, G.: Real-time consensus-based scene reconstruction using commodity graphics hardware. Proc. Pacific Conf. Computer Graphics and Applications pp. 225–234 (2002)

88.

Sabihuddin, S., Islam, J., MacLean, W.: Dynamic programming approach to high frame-rate stereo correspondence: A pipelined architecture implemented on a field programmable gate array. Proc. Canadian Conf. Electrical and Computer Engineering pp. 001,461–001,466 (2008)

89.

Safari, S., Fijany, A., Diotalevi, F., Hosseini, F.: Highly parallel and fast implementation of stereo vision algorithms on MIMD many-core Tilera architecture. Proc. IEEE Aerospace Conf. pp. 1–11 (2012)

90.

Scharstein, D., Szeliski, R.: The Middlebury Stereo Pages. http://vision.middlebury.edu/stereo/

91.

Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Intl. Journal of Computer Vision, Springer 47(1), 7–42 (2002)

92.

Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. Proc. IEEE Conf. Computer Vision and Pattern Recognition 1, I–195–I–202 (2003)

93.

Szeliski, R.: Computer vision: Algorithms and applications. Springer, London and New York (2011)CrossRef

94.

Szeliski, R., Scharstein, D.: Sampling the disparity space image. IEEE Trans. Pattern Analysis and Machine Intelligence 26(3), 419–425 (2004)CrossRef

95.

Tomasi, M., Vanegas, M., Barranco, F., Daz, J., Ros, E.: Massive parallel-hardware architecture for multiscale stereo, optical flow and image-structure computation. IEEE Trans. Circuits and Systems for Video Technology 22(2), 282–294 (2012)CrossRef

96.

Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. Proc. IEEE Conf. Computer Vision and Pattern Recognition pp. 1–8 (2008)

97.

Tseng, Y.C., Chang, T.S.: Architecture design of belief propagation for real-time disparity estimation. IEEE Trans. Circuits and Systems for Video Technology 20(11), 1555–1564 (2010)CrossRef

98.

Ttofis, C., Hadjitheophanous, S., Georghiades, A., Theocharides, T.: Edge-directed hardware architecture for real-time disparity map computation. IEEE Trans. Computers PP(99), 1 (2012)

99.

Ttofis, C., Theocharides, T.: Towards accurate hardware stereo correspondence: A real-time FPGA implementation of a segmentation-based adaptive support weight algorithm. Proc. Conf. Design, Automation & Test in Europe pp. 703–708 (2012)

100.

Vaish, V., Levoy, M., Szeliski, R., Zitnick, C., Sing, B.K.: Reconstructing occluded surfaces using synthetic apertures: Stereo, focus and robust measures. Proc. IEEE Conf. Computer Vision and Pattern Recognition 2, 2331–2338 (2006)

101.

Villalpando, C., Morfopolous, A., Matthies, L., Goldberg, S.: FPGA implementation of stereo disparity with high throughput for mobility applications. Proc. IEEE Aerospace Conf. pp. 1–10 (2011)

102.

Weber, M., Humenberger, M., Kubinger, W.: A very fast census-based stereo matching implementation on a graphics processing unit. Proc. IEEE Intl. Conf. Computer Vision Workshops pp. 786–793 (2009)

103.

Wedel, A., Rabe, C., Vaudrey, T., Brox, T., Franke, U., Cremers, D.: Efficient dense scene flow from sparse or dense stereo data. In: 10th European Conf. on Computer Vision, pp. 739–751. Springer-Verlag, Berlin, Heidelberg (2008)

104.

Woodfill, J., Gordon, G., Buck, R.: Tyzx DeepSea high speed stereo vision system. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 41 (2004)

105.

Woodfill, J., Gordon, G., Jurasek, D., Brown, T., Buck, R.: The Tyzx DeepSea G2 vision system, a taskable, embedded stereo camera. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop p. 126 (2006)

106.

Woodfill, J., Herzen, B.v.: Real-time stereo vision on the PARTS reconfigurable computer. Proc. IEEE Symp. FPGAs for Custom Computing Machines pp. 201–210 (1997)

107.

Xu, Y., Chen, H., Klette, R., Liu, J., Vaudrey, T.: Belief propagation implementation using CUDA on an Nvidia GTX 280. Proc. Advances in Artificial Intelligence 5866, 180–189 (2009)

108.

Yamaguchi, K., McAllester, D., Urtasun, R.: Efficient joint segmentation, occlusion labeling, stereo and flow estimation. Proc. European Conf. on Computer Vision (2014)

109.

Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nister, D.: Real-time global stereo matching using hierarchical belief propagation. Proc. The British Machine Vision Conf. pp. 989–998 (2006)

110.

Yunde, J., Xiaoxun, Z., Mingxiang, L., Luping: A miniature stereo vision machine (MSVM-III) for dense disparity mapping. Proc. IEEE Intl. Conf. Pattern Recognition 1, 728–731 (2004)

111.

Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. Proc. European Conf. on Computer Vision pp. 151–158 (1994)

112.

Zach, C., Klaus, A., Hadwiger, M., Karner, K.: Accurate dense stereo reconstruction using graphics hardware. Proc. EUROGRAPHICS pp. 227–234 (2003)

113.

Zach, C., Sormann, M., Karner, K.: Scanline optimization for stereo on graphics hardware. Proc. Intl. Symp. 3D Data Processing, Visualization, and Transmission pp. 512–518 (2006)

114.

Zatt, B., Shafique, M., Bampi, S., Henkel, J.: Multi-level pipelined parallel hardware architecture for high throughput motion and disparity estimation in Multiview Video Coding. Proc. Conf. Design, Automation & Test in Europe pp. 1–6 (2011)

115.

Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. Journal of Machine Learning Research (2016)

116.

Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. Trans. Circuits Syst. Video Techn., IEEE 19, 1073–1079 (2009)

117.

Zhang, Z.: Determining the epipolar geometry and its uncertainty: A review. Intl. Journal of Computer Vision, Springer 27(2), 161–195 (1998)

118.

Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRef

Title: Architectures for Stereo Vision
Authors: Christian Banz
Nicolai Behmann
Holger Blume
Peter Pirsch
Publisher: Springer International Publishing
Book: Handbook of Signal Processing Systems
Print ISBN: 978-3-319-91733-7

Electronic ISBN: 978-3-319-91734-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-91734-4_16