Journal of Real-Time Image Processing

, Volume 11, Issue 1, pp 5–25 | Cite as

Review of stereo vision algorithms and their suitability for resource-limited systems

  • Beau Tippetts
  • Dah Jye Lee
  • Kirt Lillywhite
  • James Archibald
Survey Paper

Abstract

A significant amount of research in the field of stereo vision has been published in the past decade. Considerable progress has been made in improving accuracy of results as well as achieving real-time performance in obtaining those results. This work provides a comprehensive review of stereo vision algorithms with specific emphasis on real-time performance to identify those suitable for resource-limited systems. An attempt has been made to compile and present accuracy and runtime performance data for all stereo vision algorithms developed in the past decade. Algorithms are grouped into three categories: (1) those that have published results of real-time or near real-time performance on standard processors, (2) those that have real-time performance on specialized hardware (i.e. GPU, FPGA, DSP, ASIC), and (3) those that have not been shown to obtain near real-time performance. This review is intended to aid those seeking algorithms suitable for real-time implementation on resource-limited systems, and to encourage further research and development of the same by providing a snapshot of the status quo.

References

  1. 1.
    Ambrosch, K., Kubinger, W.: Accurate hardware-based stereo vision. Comput. Vision Image Underst. 114, 1303–1316 (2010) (aCM ID: 1866603)Google Scholar
  2. 2.
    Ambrosch, K., Humenberger, M., Kubinger, W., Steininger, A.: Sad-based stereo matching using fpgas. In: Kisaanin, B., Bhattacharyya, S.S., Chai, S., (eds) Embedded, Computer Vision, pp. 121–138. Springer, London (2009) (Advances in Pattern Recognition)Google Scholar
  3. 3.
    Ambrosch, K., Zinner, C., Leopold, H.: A miniature embedded stereo vision system for automotive applications. In: Proceedings of IEEE 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), pp. 000786–000789 (2010)Google Scholar
  4. 4.
    Ansar, A., Castano, A., Matthies, L.: Enhanced real-time stereo using bilateral filtering. In: Proceedings of 2nd International Symposium. 3D Data Processing, Visualization and Transmission 3DPVT 2004, pp. 455–462 (2004)Google Scholar
  5. 5.
    Banno, A., Ikeuchi, K.: Disparity map refinement and 3d surface smoothing via directed anisotropic diffusion. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1870–1877 (2009)Google Scholar
  6. 6.
    Ben-Ari, R., Sochen, N.: Stereo matching with Mumford–Shah regularization and occlusion handling. IEEE Transact. Pattern Anal. Mach. Intell. 32(11), 2071–2084 (2010)CrossRefGoogle Scholar
  7. 7.
    Bhusnurmath, A., Taylor, C.J.: Solving stereo matching problems using interior point methods. In: Fourth International Symposium on 3D Data Processing, Visualization and Transmission, pp. 321–329 (2008)Google Scholar
  8. 8.
    Bleyer, M., Gelautz, M.: A layered stereo matching algorithm using image segmentation and global visibility constraints. ISPRS J Photogramm Remote Sens 59(3), 128–150 (2005)CrossRefGoogle Scholar
  9. 9.
    Bleyer, M., Gelautz, M., Rother, C., Rhemann, C.: A stereo approach that handles the matting problem via image warping. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 501–508 (2009)Google Scholar
  10. 10.
    Bleyer, M., Rother, C., Kohli, P.: Surface stereo with soft segmentation. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 1570–1577 (2010)Google Scholar
  11. 11.
    Bleyer, M., Rhemann, C., Rother, C.: Patchmatch stereo—stereo matching with slanted support windows. In: British Machine Vision Conference (BMVC) (2011a)Google Scholar
  12. 12.
    Bleyer, M., Rother, C., Kohli, P., Scharstein, D., Sinha, S.: Object stereo—joint stereo matching and object segmentation. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2011b)Google Scholar
  13. 13.
    Bobick, A.F., Intille, S.S.: Large occlusion stereo. Int. J. Comput. Vision 33, 181–200, (1999). doi:10.1023/A:1008150329890 Google Scholar
  14. 14.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transact. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  15. 15.
    Brockers, R.: Cooperative stereo matching with color-based adaptive local support. In: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, pp. 1019–1027. Springer, Berlin, (2009) aCM ID: 1618054Google Scholar
  16. 16.
    Brockers, R., Hund, M., Mertsching, B.: Stereo vision using cost-relaxation with 3d support regions. Image and Vision Computing New Zealand (IVCNZ) (2005)Google Scholar
  17. 17.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in computational stereo. IEEE Transact. Pattern Anal. Mach. Intell. 25(8), 993–1008 (2003)CrossRefGoogle Scholar
  18. 18.
    Bruch, M., Lum, J., Yee, S., Tran, N.: Advances in autonomy for small ugvs. SPIE Proc 5804: Unmanned Ground Vehicle Technology VII, Orlando (2005)Google Scholar
  19. 19.
    Cassisa, C.: Local vs global energy minimization methods: Application to stereo matching. In: Proceedings of IEEE International Progress in Informatics and Computing (PIC) Conference, vol. 2, pp. 678–683 (2010)Google Scholar
  20. 20.
    Center for Visual Information Technology: CUDA Cuts. http://cvit.iiit.ac.in/index.php?page=resources (2011)
  21. 21.
    Chan, S.O.Y., Wong, Y.P., Daniel, J.K.: Dense stereo correspondence based on recursive adaptive size multi-windowing. In: Proceedings of Image and Vision Computing, vol. 1, pp. 256–260. New Zealand (2003)Google Scholar
  22. 22.
    Chang, N., Lin, T.M., Tsai, T.H., Tseng, Y.C., Chang, T.S.: Real-time dsp implementation on local stereo matching. In: Proceedings of IEEE International Multimedia and Expo Conference, pp. 2090–2093 (2007)Google Scholar
  23. 23.
    Chang, N.Y.C., Tsai, T.H., Hsu, B.H., Chen, Y.C., Chang, T.S.: Algorithm and architecture of disparity estimation with mini-census adaptive support weight. IEEE Transact. Circuits Syst. Video Technol. 20(6), 792–805 (2010)CrossRefGoogle Scholar
  24. 24.
    Chang, X., Zhou, Z., Wang, L., Shi, Y., Zhao, Q.: Real-time accurate stereo matching using modified two-pass aggregation and winner-take-all guided dynamic programming. In: Proceedings of International 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT) Conference, pp. 73–79 (2011)Google Scholar
  25. 25.
    Chen, W., Zhang, M.J., Xiong, Z.H.: Fast semi-global stereo matching via extracting disparity candidates from region boundaries. IET Comput. Vision 5(2), 143–150 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Cornells, N., Van Gool, L.: Real-time connectivity constrained depth map computation using programmable graphics hardware. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition CVPR 2005, vol. 1, pp. 1099–1104 (2005)Google Scholar
  27. 27.
    Cuadrado, C., Zuloaga, A., Martin, J.L., Lazaro, J., Jimenez, J.: Real-time stereo vision processing system in a fpga. In: Proceedings of IECON 2006—32nd Annual Conference. IEEE Industrial Electronics, pp. 3455–3460 (2006)Google Scholar
  28. 28.
    De-Maeztu, L., Mattoccia, S., Villanueva, A., Cabeza, R.: Linear stereo matching. In: A13th International Conference on Computer Vision (ICCV2011) (2011a)Google Scholar
  29. 29.
    De-Maeztu, L., Villanueva, A., Cabeza, R.: Stereo matching using gradient similarity and locally adaptive support-weight. Pattern Recognit. Lett. 32(13), 1643–1651 (2011)CrossRefGoogle Scholar
  30. 30.
    Demoulin, C., Droogenbroeck, M.V.: A method based on multiple adaptive windows to improve the determination of disparity maps. In: Proceedings of IEEE Workshop on Circuit, Systems and Signal Processing (2005)Google Scholar
  31. 31.
    Deng, Y., Lin, X.: A fast line segment based dense stereo algorithm using tree dynamic programming. In: Computer Vision—ECCV 2006, Lecture Notes in Computer Science, vol. 3953, Springer, Heidelberg, pp. 201–212. (2006). doi:10.1007/11744078_16
  32. 32.
    Desouza, G.N., Kak, A.C.: Vision for mobile robot navigation: a survey. IEEE Transact. Pattern Anal. Mach. Intell. 24(2), 237–267 (2002)CrossRefGoogle Scholar
  33. 33.
    Einecke, N., Eggert, J.: A two-stage correlation method for stereoscopic depth estimation. In: Digital Image Computing: Techniques and Applications, IEEE Computer Society, Los Alamitos, CA, vol. 0, pp. 227–234 (2010)Google Scholar
  34. 34.
    El-Etriby, S., Al-Hamadi, A.K., Michaelis, B.: Dense depth map reconstruction by phase difference-based algorithm under influence of perspective distortion. Mach. Grap. Vision Int. J. 15(3), 349–361 (2006)Google Scholar
  35. 35.
    El-Etriby, S., Al-Hamadi, A., Michaelis, B.: Dense stereo correspondence with slanted surface using phase-based algorithm. In: Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on, pp. 1807–1813 (2007)Google Scholar
  36. 36.
    Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. Int. J. Comput. Vision 70(1), 41–54 (2006)CrossRefGoogle Scholar
  37. 37.
    Forstmann, S., Kanou, Y., Ohya, J., Thuering, S., Schmitt, A.: Real-time stereo by using dynamic programming. In: Proceedings of Conference Computer Vision and Pattern Recognition Workshop CVPRW ’04 (2004)Google Scholar
  38. 38.
    Gales, G., Crouzil, A., Chambon, S.: A region-based randomized voting scheme for stereo matching. In: Advances in Visual Computing, Lecture Notes in Computer Science, vol. 6454, Springer, Berlin, pp. 182–191 (2010) doi:10.1007/978-3-642-17274-8_18
  39. 39.
    Gehrig, S., Franke, U.: Improving sub-pixel accuracy for long range stereo. In: ICCV VRML workshop (2007)Google Scholar
  40. 40.
    Gerrits, M., Bekaert, P.: Local stereo matching with segmentation-based outlier rejection. In: Proceedings of 3rd Canadian Conference Computer and Robot Vision (2006)Google Scholar
  41. 41.
    Goldberg, S.B., Matthies, L.: Stereo and imu assisted visual odometry on an omap3530 for small robots. In: Proceedings IEEE Computer Society Conference Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 169–176 (2011)Google Scholar
  42. 42.
    Gong, M., Yang, R.: Image-gradient-guided real-time stereo on graphics hardware. In: Proceedings of Fifth International Conference 3-D Digital Imaging and Modeling 3DIM 2005, pp. 548–555 (2005a)Google Scholar
  43. 43.
    Gong, M., Yang, Y.H.: Near real-time reliable stereo matching using programmable graphics hardware. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 1, pp. 924–931 (2005b)Google Scholar
  44. 44.
    Gong, M., Yang, R., Wang, L., Gong, M.: A performance study on different cost aggregation approaches used in real-time stereo matching. Int. J. Comput. Vision (IJCV) (2007)Google Scholar
  45. 45.
    Gong, M., Zhang, Y., Yang, Y.H.: Near-real-time stereo matching with slanted surface modeling and sub-pixel accuracy. Pattern Recognit. 44(10–11), 2701–2710 (semi-Supervised Learning for Visual Content Analysis and Understanding)Google Scholar
  46. 46.
    Grauer-Gray, S., Kambhamettu, C.: Hierarchical belief propagation to reduce search space using cuda for stereo and motion estimation. In: 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–8 (2009)Google Scholar
  47. 47.
    Gu, Z., Su, X., Liu, Y., Zhang, Q.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recognit. Lett. 29(9), 1230–1235 (2008)CrossRefGoogle Scholar
  48. 48.
    Gupta, R., Cho, S.Y.: A correlation-based approach for real-time stereo matching. In: Advances in Visual Computing, Lecture Notes in Computer Science, Springer Berlin, vol. 6454, pp. 129–138 (2010a). doi:10.1007/978-3-642-17274-8_13
  49. 49.
    Gupta, R.K., Cho, S.Y.: Real-time stereo matching using adaptive binary window. In: Proceedings of the 3DPVT, Paris (2010b)Google Scholar
  50. 50.
    Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 2, pp 807–814 (2005)Google Scholar
  51. 51.
    Hirschmuller, H. Stereo vision in structured environments by consistent semi-global matching. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2386–2393 (2006)Google Scholar
  52. 52.
    Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Transact. Pattern Anal. Mach. Intell. 31(9), 1582–1599 (2009)CrossRefGoogle Scholar
  53. 53.
    Hirschmuller, H., Innocent, P.R., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. Int. J. Comput. Vision 47, 229–246 (2002)Google Scholar
  54. 54.
    Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local stereo matching using geodesic support weights. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2093–2096 (2009)Google Scholar
  55. 55.
    Howard, A.: Real-time stereo visual odometry for autonomous ground vehicles. In: Proceedings of IEEE/RSJ International Conference Intelligent Robots and Systems IROS 2008, pp. 3946–3952 (2008)Google Scholar
  56. 56.
    Hu. W., Zhang, K., Sun, L., Li, J., Li, Y., Yang, S.: Virtual support window for adaptive-weight stereo matching. In: Visual Communications and Image Processing (VCIP) (2011)Google Scholar
  57. 57.
    Humenberger, M., Zinner, C., Kubinger, W.: Performance evaluation of a census-based stereo matching algorithm on embedded and multi-core hardware. In: Proceedings of 6th International Symposium Image and Signal Processing and Analysis ISPA 2009, pp. 388–393 (2009)Google Scholar
  58. 58.
    Humenberger, M., Engelke, T., Kubinger, W.: A census-based stereo vision algorithm using modified semi-global matching and plane fitting to improve matching quality. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 77–84 (2010a)Google Scholar
  59. 59.
    Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Comput. Vision Image Underst. 114(11), 1180–1202 (2010b)Google Scholar
  60. 60.
    Ishikawa, H.: Higher-order gradient descent by fusion-move graph cut. In: Proceedings of IEEE 12th International Computer Vision Conference, pp. 568–574 (2009)Google Scholar
  61. 61.
    Ishikawa, H., Geiger, D.: Occlusions, discontinuities, and epipolar lines in stereo. In: In European Conference on Computer Vision, pp. 232–248 (1998)Google Scholar
  62. 62.
    IST Austria (2009) Maxflow. http://pub.ist.ac.at/~vnk/software.html
  63. 63.
    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 Transact. Circuits Syst. Video Technol. 20(1), 15–26 (2010)CrossRefGoogle Scholar
  64. 64.
    Kalarot, R., Morris, J.: Comparison of fpga and gpu implementations of real-time stereo vision. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–15 (2010)Google Scholar
  65. 65.
    Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition CVPR 2001, vol. 1 (2001)Google Scholar
  66. 66.
    Khaleghi, B., Ahuja, S., Wu, Q.: An improved real-time miniaturized embedded stereo vision system (mesvs-ii). In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition Workshops CVPRW ’08, pp 1–8 (2008)Google Scholar
  67. 67.
    Kim, J., Hwangbo, M., Kanade, T.: Parallel algorithms to a parallel hardware: Designing vision algorithms for a gpu. In: Workshop on Embedded Computer Vision (ECV), 2009 (held in conjunction with ICCV) (2009)Google Scholar
  68. 68.
    Kim, J.C., Lee, K.M., Choi, B.T., Lee, S.U.: A dense stereo matching using two-pass dynamic programming with generalized ground control points. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition CVPR 2005, vol. 2, pp. 1075–1082 (2005)Google Scholar
  69. 69.
    Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: 18th International Conference on Pattern Recognition. ICPR 2006. vol. 3, pp. 15–18 (2006)Google Scholar
  70. 70.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proceedings of Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. vol. 2, pp. 508–515 (2001)Google Scholar
  71. 71.
    Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. Eur. Conf. Comput. Vision 3, 82–96 (2002)Google Scholar
  72. 72.
    Konolige, K.: Small vision systems: hardware and implementation. In: 8th International Symposium on Robotics Research, pp. 111–116 (1997)Google Scholar
  73. 73.
    Kosov, S., Thormahlen, T., Seidel, H.P.: Accurate real-time disparity estimation with variational methods. In: ISVC ’09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I (2009)Google Scholar
  74. 74.
    Kuhn, M., Moser, S., Isler, O., Gurkaynak, F.K., Burg, A., Felber, N., Kaeslin, H., Fichtner, W.: Efficient asic implementation of a real-time depth mapping stereo vision system. In: Proceedings of IEEE 46th Midwest Symposium Circuits and Systems, vol. 3, pp. 1478–1481 (2003)Google Scholar
  75. 75.
    Larsen, E., Mordohai, P., Pollefeys, M., Fuchs, H.: Temporally consistent reconstruction from multiple video streams using enhanced belief propagation. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. pp. 1–8 (2007)Google Scholar
  76. 76.
    Lei, C., Selzer, J., Yang, Y.H.: Region-tree based stereo using dynamic programming optimization. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2378–2385 (2006)Google Scholar
  77. 77.
    Liang, C.K., Cheng, C.C., Lai, Y.C., Chen, L.G., Chen, H.H.: Hardware-efficient belief propagation. IEEE Transact. Circuits Syst. Video Technol. 21(5), 525–537 (2011)CrossRefGoogle Scholar
  78. 78.
    Liu, T., Zhang, P., Luo, L.: Dense stereo correspondence with contrast context histogram, segmentation-based two-pass aggregation and occlusion handling. In: Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology, Springer, Berlin, PSIVT ’09, pp. 449–461 (2008) (aCM ID: 1505991)Google Scholar
  79. 79.
    Lu, J., Lafruit, G., Catthoor, F.: Fast variable center-biased windowing for high-speed stereo on programmable graphics hardware. In: Proceedings of IEEE International Conference Image Processing ICIP 2007, vol. 6 (2007a)Google Scholar
  80. 80.
    Lu, J., Rogmans, S., Lafruit, G., Catthoor, F.: Real-time stereo correspondence using a truncated separable Laplacian kernel approximation on graphics hardware. In: Proceedings of IEEE Int Multimedia and Expo Conference, pp. 1946–1949 (2007b)Google Scholar
  81. 81.
    Lu, J., Lafruit, G., Catthoor, F.: Anisotropic local high-confidence voting for accurate stereo correspondence. In: Proceedings of SPIE, San Jose, pp. 68,120J–68,120J–12 (2008)Google Scholar
  82. 82.
    van der Mark, W., Gavrila, D.M.: Real-time dense stereo for intelligent vehicles. IEEE Transact. Intell. Transp. Syst. 7(1), 38–50 (2006)CrossRefGoogle Scholar
  83. 83.
    Masrani, D., MacLean, W.: A real-time large disparity range stereo-system using fpgas. In: IEEE International Conference on Computer Vision Systems, 2006 ICVS ’06. p. 13 (2006)Google Scholar
  84. 84.
    Mattoccia, S.: A locally global approach to stereo correspondence. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1763–1770 (2009)Google Scholar
  85. 85.
    Mattoccia, S., Tombari, F., Stefano, L.D.: Stereo vision enabling precise border localization within a scanline optimization framework. In: Computer Vision ACCV 2007, Lecture Notes in Computer Science, Springer, Berlin, vol. 4844, pp. 517–527 (2007) doi:10.1007/978-3-540-76390-1_51
  86. 86.
    Mattoccia, S., Giardino, S., Gambini, A.: Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering. In: Computer Vision—ACCV 2009, Lecture Notes in Computer Science, Springer, Berlin, vol. 5995, pp. 371–380 (2010)Google Scholar
  87. 87.
    Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware Technical Report, Samsung Advanced Institute of Technology (2011)Google Scholar
  88. 88.
    Min, D., Sohn, K.: Cost aggregation and occlusion handling with wls in stereo matching. IEEE Transact. Image Process. 17(8), 1431–1442 (2008)MathSciNetCrossRefGoogle Scholar
  89. 89.
    Min, D., Luy, J., Do, M.N.: A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy? In: International Conference on Computer Vision (2011)Google Scholar
  90. 90.
    Mingxiang, L., Yunde, J.: Stereo vision system on programmable chip (svsoc) for small robot navigation. In: Proceedings of IEEE/RSJ International Intelligent Robots and Systems Conference, pp. 1359–1365 (2006)Google Scholar
  91. 91.
    Miyajima, Y., Maruyama, T.: A real-time stereo vision system with fpga. In: Field-Programmable Logic and Applications, Lecture Notes in Computer Science, vol. 2778, Springer, Berlin, pp. 448–457 (2003)Google Scholar
  92. 92.
    Miyazaki, D., Matsushita, Y., Ikeuchi, K.: Interactive shadow removal from a single image using hierarchical graph cut. In: Computer Vision—ACCV 2009, Lecture Notes in Computer Science, Springer, Berlin, vol. 5994, pp. 234–245 (2010)Google Scholar
  93. 93.
    Montserrat, T., Civit, J., Escoda, O., Landabaso, J.L.: Depth estimation based on multiview matching with depth/color segmentation and memory efficient belief propagation. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 2353–2356 (2009)Google Scholar
  94. 94.
    Mordohai, P., Medioni, G.: Stereo using monocular cues within the tensor voting framework. IEEE Transact. Pattern Anal. Mach. Intell. 28(6), 968–982 (2006) (pMID: 16724590)Google Scholar
  95. 95.
    Muhlmann, K., Maier, D., Hesser, R., Manner, R.: Calculating dense disparity maps from color stereo images, an efficient implementation. In: Proceedings of IEEE Workshop Stereo and Multi-Baseline Vision (SMBV 2001), pp. 30–36 (2001)Google Scholar
  96. 96.
    Mukherjee, D., Wang, G., Wu, Q.: Stereo matching algorithm based on curvelet decomposition and modified support weights. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 758–761 (2010)Google Scholar
  97. 97.
    Nalpantidis, L., Gasteratos, A.: Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. Robotics Auton. Syst. 58(5), 457–464 (2010)CrossRefGoogle Scholar
  98. 98.
    Nalpantidis, L., Gasteratos, A.: Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image Vision Comput. 28(6), 940–951 (2010)CrossRefGoogle Scholar
  99. 99.
    Nalpantidis, L., Sirakoulis, G.C., Gasteratos, A.: Review of stereo vision algorithms: From software to hardware. Int. J. Optomechatron. 2(4), 435–462 (2008)CrossRefGoogle Scholar
  100. 100.
    Naoulou, A., Boizard, J.L., Fourniols, J.Y., Devy, M.: An alternative to sequential architectures to improve the processing time of passive stereovision algorithms. In: Proceedings of International Conference Field Programmable Logic and Applications FPL ’06, pp. 1–4 (2006)Google Scholar
  101. 101.
    Ogale, A.S., Aloimonos, Y.: Shape and the stereo correspondence problem. Int. J. Comput. Vision 65 (2005)Google Scholar
  102. 102.
    Olague, G., de Vega, F.F., Prez, C.B., Lutton, E.: The infection algorithm: an artificial epidemic approach for dense stereo matching. In: Parallel Problem Solving from Nature - PPSN VIII, Lecture Notes in Computer Science, vol. 3242, Springer, Berlin, pp. 622–632 (2004)Google Scholar
  103. 103.
  104. 104.
    Papadakis, N., Caselles, V.: Multi-label depth estimation for graph cuts stereo problems. J. Math. Imaging Vision 38(1), 70–82 (2010)MathSciNetCrossRefGoogle Scholar
  105. 105.
    Park, S., Jeong, H.: Real-time stereo vision fpga chip with low error rate. In: Proceedings of International Conference Multimedia and Ubiquitous Engineering MUE ’07, pp. 751–756 (2007)Google Scholar
  106. 106.
    PassMark Software: Cpu benchmarks. http://www.cpubenchmark.net/cpu_list.php (2012)
  107. 107.
    Perez, J.M., Sanchez, P., Martinez, M.: High memory throughput fpga architecture for high-definition belief-propagation stereo matching. In: Proceedings of 3rd International Signals, Circuits and Systems (SCS) Conference, pp. 1–6 (2009)Google Scholar
  108. 108.
    Perri, S., Colonna, D., Zicari, P., Corsonello, P.: Sad-based stereo matching circuit for fpgas. In: Proceedings of 13th IEEE International Conference Electronics, Circuits and Systems ICECS ’06, pp. 846–849 (2006)Google Scholar
  109. 109.
    Pock, T., Schoenemann, T., Graber, G., Bischof, H., Cremers, D.: A convex formulation of continuous multi-label problems. In: Proceedings of the 10th European Conference on Computer Vision: Part III, Springer, Berlin, pp. 792–805 (2008) (aCM ID: 1478235)Google Scholar
  110. 110.
    Psota, E.T., Kowalczuk, J., Carlson, J., Perez, L.C.: A local iterative refinement method for adaptive support-weight stereo matching. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (2011)Google Scholar
  111. 111.
    Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  112. 112.
    Richardt, C., Orr, D., Davies, I., Criminisi, A., Dodgson, N.: Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid. In: Computer Vision - ECCV 2010, Lecture Notes in Computer Science, vol. 6313, Springer, Berlin, pp. 510–523 (2010)Google Scholar
  113. 113.
    Sabihuddin, S., MacLean, W.J.: Maximum-likelihood stereo correspondence using field programmable gate arrays. In: The 5th International Conference on Computer Vision Systems (2007)Google Scholar
  114. 114.
    Salmen, J., Schlipsing, M., Edelbrunner, J., Hegemann, S., Lke, S.: Real-time stereo vision: Making more out of dynamic programming. In: Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 5702, Springer, Berlin, pp. 1096–1103 (2009)Google Scholar
  115. 115.
    Samarawickrama, M.G.: Performance evaluation of vision algorithms on fpga. Master’s thesis, University of Moratuwa, Sri Lanka (2010)Google Scholar
  116. 116.
    Scharstein, D.: Middlebury stereo evaluation. http://vision.middlebury.edu/stereo/eval/ (2012)
  117. 117.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 7–42 (2002)Google Scholar
  118. 118.
    Stankiewicz, O., Wegner, K.: Depth map estimation software version 2. Techinical report, ISO/IEC MPEG meeting M15338 (2008)Google Scholar
  119. 119.
    Stankiewicz, O., Wegner, K.: Depth map estimation software version 3. Techinical report, ISO/IEC MPEG meeting M15540 (2009)Google Scholar
  120. 120.
    Stefano, L.D., Marchionni, M., Mattoccia, S.: A fast area-based stereo matching algorithm. Image Vision Comput. 22(12), 983–1005 (2004)CrossRefGoogle Scholar
  121. 121.
    Strecha, C., Fransens, R., Gool, L.V.: Combined depth and outlier estimation in multi-view stereo. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2394–2401 (2006)Google Scholar
  122. 122.
    Sun, C.: Fast stereo matching using rectangular subregioning and 3d maximum-surface techniques. Int. J. Comput. Vision 47, 99–117 (2002). doi:10.1023/A:1014585622703 Google Scholar
  123. 123.
    Sun, J., Li, Y., Kang, S., Shum, H.Y.: Symmetric stereo matching for occlusion handling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 2, pp. 399–406 (2005)Google Scholar
  124. 124.
    Sun, X., Mei, X., Jiao, S., Zhou, M., Wang, H.: Stereo matching with reliable disparity propagation. In: Proceedings of Int 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT) Conference, pp. 132–139 (2011)Google Scholar
  125. 125.
    Szeliski, R., Zabih, R.: An experimental comparison of stereo algorithms. Vision Algorithms: Theory and Practice, pp. 1–19 (2000)Google Scholar
  126. 126.
    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transact. Pattern Anal. Mach. Intell. 30(6), 1068–1080 (2008)Google Scholar
  127. 127.
    Taguchi, Y., Wilburn, B., Zitnick, C.: Stereo reconstruction with mixed pixels using adaptive over-segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008. pp. 1–8 (2008)Google Scholar
  128. 128.
    Tippetts, B.J., Lee, D.J., Archibald, J.K., Lillywhite, K.D.: Dense disparity real-time stereo vision algorithm for resource-limited systems. IEEE Transact. Circuits Syst. Video Technol. 21(10), 1547–1555 (2011)CrossRefGoogle Scholar
  129. 129.
    Tombari, F., Mattoccia, S., Stefano, L.D.: Segmentation-based adaptive support for accurate stereo correspondence. In: Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology, Springer, Berlin, PSIVT’07, pp. 427–438 (2007)Google Scholar
  130. 130.
    Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition CVPR 2008, pp. 1–8 (2008a)Google Scholar
  131. 131.
    Tombari, F., Mattoccia, S., Stefano, L.D., Addimanda, E.: Near real-time stereo based on effective cost aggregation. In: 19th International Conference on Pattern Recognition, 2008. ICPR 2008. pp. 1–4 (2008b)Google Scholar
  132. 132.
    Trinh, H., McAllester, D.: Unsupervised learning of stereo vision with monocular cues. In: British Machine Vision Conference (2009)Google Scholar
  133. 133.
    Vanetti, M., Gallo, I., Binaghi, E.: Dense two-frame stereo correspondence by self-organizing neural network. In: Proceedings of the 15th International Conference on Image Analysis and Processing, Springer, Berlin, ICIAP ’09, pp. 1035–1042 (2009) (aCM ID: 1618209)Google Scholar
  134. 134.
    Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition, vol. 1 (2003)Google Scholar
  135. 135.
    Veksler, O.: Stereo correspondence by dynamic programming on a tree. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005. vol. 2, pp. 384–390 (2005)Google Scholar
  136. 136.
    Venkatesh, Y.V., Raja, S.K., Kumar, A.J.: On the application of a modified self-organizing neural network to estimate stereo disparity. IEEE Transact. Image Process. 16(11), 2822–2829 (2007)MathSciNetCrossRefGoogle Scholar
  137. 137.
    Vineet, V., Narayanan, P.J.: Cuda cuts: Fast graph cuts on the gpu. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition Workshops CVPRW ’08, pp. 1–8 (2008)Google Scholar
  138. 138.
    Wang, L., Yang, R.: Global stereo matching leveraged by sparse ground control points. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  139. 139.
    Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’06), IEEE Computer Society, Washington, 3DPVT ’06, pp. 798–805 (2006) (aCM ID: 1249375)Google Scholar
  140. 140.
    Wang, Z.F., Zheng, Z.G.: A region based stereo matching algorithm using cooperative optimization. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recogn. 0, 1–8 (2008)Google Scholar
  141. 141.
    Woodfill, J., Von Herzen, B.: Real-time stereo vision on the parts reconfigurable computer. In: Proceedings of 5th Annual IEEE Symp FPGAs for Custom Computing Machines, pp. 201–210 (1997)Google Scholar
  142. 142.
    Woodfill, JI., Gordon, G., Jurasek, D., Brown, T., Buck, R.: The tyzx deepsea g2 vision system, ataskable, embedded stereo camera. In: Proceedings of Conference Computer Vision and Pattern Recognition Workshop CVPRW ’06 (2006)Google Scholar
  143. 143.
    Woodford, O., Torr, P., Reid, I., Fitzgibbon, A.: Global stereo reconstruction under second-order smoothness priors. IEEE Transact. Pattern Anal. Mach. Intell. 31(12), 2115–2128 (2009)Google Scholar
  144. 144.
    Xu, L., Jia, J.: Stereo matching: An outlier confidence approach. In: Computer Vision—ECCV 2008, Lecture Notes in Computer Science, vol. 5305. Springer, Berlin, pp. 775–787 (2008)Google Scholar
  145. 145.
    Xu, Y., Wang, D., Feng, T., Shum, H.Y.: Stereo computation using radial adaptive windows. In: Proceedings of 16th International Pattern Recognition Conference, vol. 3, pp. 595–598 (2002)Google Scholar
  146. 146.
    Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nister, D.: Real-time global stereo matching using hierarchical belief propagation. In: The British Machine Vision Conference, pp. 989–998 (2006)Google Scholar
  147. 147.
    Yang, Q., Yang, R., Davis, J., Nister, D.: Spatial-depth super resolution for range images. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR ’07. pp. 1–8 (2007)Google Scholar
  148. 148.
    Yang, Q., Engels, C., Akbarzadeh, A.: Near real-time stereo for weakly-textured scenes. In: British Machine Vision Conference (2008)Google Scholar
  149. 149.
    Yang, Q., Wang, L., Yang, R., Stewenius, H., Nister, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Transact. Pattern Anal. Mach. Intell. 31(3), 492–504 (2009)CrossRefGoogle Scholar
  150. 150.
    Yang, Q., Wang, L., Ahuja, N.: A constant-space belief propagation algorithm for stereo matching. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR), pp. 1458–1465 (2010)Google Scholar
  151. 151.
    Yang, R., Pollefeys, M.: Multi-resolution real-time stereo on commodity graphics hardware. In: Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition, vol. 1 (2003)Google Scholar
  152. 152.
    Yang, R., Pollefeys, M., Li, S.: Improved real-time stereo on commodity graphics hardware. In: Proceedings of Conference Computer Vision and Pattern Recognition Workshop CVPRW ’04 (2004)Google Scholar
  153. 153.
    Yoon, K.J., Kweon, I.S.: Locally adaptive support-weight approach for visual correspondence search. In: Computer Vision and Pattern Recognition, pp. 924–931 (2005)Google Scholar
  154. 154.
    Yoon, K.J., Kweon, I.S.: Adaptive support-weight approach for correspondence search. IEEE Transact. Pattern Anal. Mach. Intell. 28(4), 650–656 (2006)CrossRefGoogle Scholar
  155. 155.
    Yoon, K.J., Kweon, I.S.: Stereo matching with the distinctive similarity measure. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. pp. 1–7 (2007)Google Scholar
  156. 156.
    Yoon, S., Park, S.K., Kang, S., Kwak, Y.K.: Fast correlation-based stereo matching with the reduction of systematic errors. Pattern Recogn. Lett. 26(14), 2221–2231 (2005)CrossRefGoogle Scholar
  157. 157.
    Yu, T., Lin, R.S., Super, B., Tang, B.: Efficient message representations for belief propagation. In: IEEE International Conference on Computer Vision. vol. 0, IEEE Computer Society, Los Alamitos, pp. 1–8 (2007)Google Scholar
  158. 158.
    Yu, W., Chen, T., Franchetti, F., Hoe, J.: High performance stereo vision designed for massively data parallel platforms. IEEE Transact. Circuits Syst. Video Technol. 20(11), 1509–1519 (2010)CrossRefGoogle Scholar
  159. 159.
    Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Transact. Circuits Syst. Video Technol. 19(7), 1073–1079 (2009)CrossRefGoogle Scholar
  160. 160.
    Zhang, K., Lu, J., Lafruit, G., Lauwereins, R., Gool, L.V.: Real-time accurate stereo with bitwise fast voting on cuda. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 794–800 (2009b)Google Scholar
  161. 161.
    Zhang, L., Zhang, K., Chang, T.S., Lafruit, G., Kuzmanov, G.K., Verkest, D.: Real-time high-definition stereo matching on fpga. In: Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays, ACM, New York, FPGA ’11, pp. 55–64 (2011)Google Scholar
  162. 162.
    Zhao, Y., Taubin, G.: Real-time stereo on GPGPU using progressive multi-resolution adaptive windows. Image Vision Comput. 29(6) 420–432 (2011)Google Scholar
  163. 163.
    Zinner, C., Humenberger, M.: Distributed real-time stereo matching on smart cameras. In: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras, ACM, New York, ICDSC ’10, pp. 182–189 (2010)Google Scholar
  164. 164.
    Zitnick, C.L., Kanade, T.: A cooperative algorithm for stereo matching and occlusion detection. IEEE Transact. Pattern Anal. Mach. Intell. 22(7), 675–684 (2000)CrossRefGoogle Scholar
  165. 165.
    Zitnick, C.L., Kang, S.B.: Stereo for image-based rendering using image over-segmentation. Int. J. Comput. Vision 75(1), 49–65 (2007)CrossRefGoogle Scholar
  166. 166.
    Zitnick, C.L., Kang, S.B., Uyttendaele, M., Winder, S., Szeliski, R.: High-quality video view interpolation using a layered representation. In: ACM Transactions on Graphics (TOG), ACM, New York, SIGGRAPH ’04, pp. 600–608 (2004) (aCM ID: 1015766)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Beau Tippetts
    • 1
  • Dah Jye Lee
    • 1
  • Kirt Lillywhite
    • 1
  • James Archibald
    • 1
  1. 1.Brigham Young UniversityProvoUSA

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