Abstract
Stereo matching approaches are an appealing choice for acquiring depth information in a number of video processing applications. It is desirable that these solutions generate dense, robust disparity maps in real time. However, occlusion regions may disturb the applications that need these maps. Among the best of these approaches is the semi-global matching (SGM) technique. This paper presents an FPGA-based stereo vision system based on SGM. This system calculates disparity maps by streaming, which are scalable to several resolutions and disparity ranges. To increase the robustness of the SGM technique even further, the present work has implemented a combination of the gradient filter and the sampling-insensitive absolute difference in the pre-processing phase. Furthermore, as a post-processing step, this paper proposes a novel streaming architecture to detect noisy and occluded regions. The FPGA-based implementations of the proposed stereo matching system in two distinct heterogeneous architecture (GPP—general purpose processor, and FPGA) were evaluated using the Middlebury stereo vision benchmark. The achieved results reported a frame rate of 25 FPS for the disparity maps processing in HD resolution (1024 \(\times\) 768 pixels), with 256 disparity levels. The results have demonstrated that the memory utilization, processing performance, and accuracy are among the best of FPGA-based stereo vision systems.
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Prisacariu, V.A., Kähler, O., Golodetz, S., Sapienza, M., Cavallari, T., Torr, P.H.S., Murray, D.W..: InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure. arXiv:1708.00783 (arXiv preprint) (2017)
Keller, C.G., Enzweiler, M., Rohrbach, M., Llorca, D.F., Schnorr, C., Gavrila, D.M.: The benefits of dense stereo for pedestrian detection. IEEE Trans. Intell. Transport. Syst. 12(4), 1096–1106 (2011)
Oleynikova, H., Honegger, D., Pollefeys, M.: Reactive avoidance using embedded stereo vision for MAV flight. In: Robotics and Automation (ICRA), 2015 IEEE International Conference on, pp. 50–56. IEEE (2015)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002)
Hirschmüller, H., Gehrig, S.: Stereo matching in the presence of sub-pixel calibration errors. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 437–444. IEEE (2009)
Sun, J., Li, Y., Kang, S.B., Shum, H.Y.: Symmetric stereo matching for occlusion handling. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 399–406. IEEE (2005)
Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)
Gehrig, S.K., Rabe, C.: Real-time semi-global matching on the CPU. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 85–92 (2010)
Spangenberg, R., Langner, T., Adfeldt, S., Rojas, R.: Large scale semi-global matching on the CPU. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 195–201 (2014)
Hernandez-Juarez, D., Chacón, A., Espinosa, A., Vázquez, D., Moure, J.C., López, A.M.: Embedded real-time stereo estimation via Semi-Global Matching on the GPU. Proced. Comput. Sci. 80, 143–153 (2016)
Bailey, D.G.: Design for Embedded Image Processing on FPGAs, 1st edn. Wiley, Oxford (2011)
Cambuim, L.F.S., Barbosa, J.P.F., Barros, E.N.S.: Hardware module for low-resource and real-time stereo vision engine using semi-global matching approach. In: Proceedings of the 30th Symposium on Integrated Circuits and Systems Design: Chip on the Sands, SBCCI ’17, pp. 53–58, New York, NY, USA. ACM (2017)
Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. Pattern Anal. Mach. Intell. 20(4), 401–406 (1998)
Honegger, D., Oleynikova, H., Pollefeys, M.: Real-time and low latency embedded computer vision hardware based on a combination of FPGA and mobile CPU. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4930–4935 (2014)
Gehrig, S.K., Eberli, F., Meyer, T.: A real-time low-power stereo vision engine using semi-global matching. In: International Conference on Computer Vision Systems, pp. 134–143. Springer (2009)
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. In: Embedded Computer Systems (SAMOS), 2010 International Conference on, pp. 93–101. IEEE (2010)
Wang, W., Yan, J., Xu, N., Wang, Y., Hsu, F.H.: Real-time high-quality stereo vision system in FPGA. In: 2013 International Conference on Field-Programmable Technology (FPT), pp. 358–361 (2013)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, New York (2003)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, Upper Saddle River (2006)
Pantilie, C.D., Nedevschi, S.: SORT-SGM: subpixel optimized real-time semiglobal matching for intelligent vehicles. IEEE Trans. Veh. Technol. 61(3), 1032–1042 (2012)
Benedetti, L., Corsini, M., Cignoni, P., Callieri, M., Scopigno, R.: Color to gray conversions in the context of stereo matching algorithms. Mach. Vis. Appl. 23(2), 327–348 (2012)
Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1582–1599 (2009)
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition, pp. 31–42. Springer (2014)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, vol. 1, pp I–I. IEEE (2003)
Scharstein, D., Pal, C.: Learning conditional random fields for stereo. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, pp. 1–8. IEEE (2007)
Hirschmüller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, pp. 1–8. IEEE (2007)
Wang, W., Yan, J., Xu, N., Wang, Y., Hsu, F.H.: Real-time high-quality stereo vision system in FPGA. IEEE Trans. Circ. Syst. Video Technol. 25(10), 1696–1708 (2015)
Rahnama, O., Cavallari, T., Golodetz, S., Walker, S., Torr, P.H.S.: R3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems. arXiv:1810.12988 (arXiv preprint) (2018)
Perri, S., Frustaci, F., Spagnolo, F., Corsonello, P.: Stereo vision architecture for heterogeneous systems-on-chip. J. Real Time Image Process. (2018)
Schönberger, J.L., Sinha, S.N., Pollefeys, M.: Learning to fuse proposals from multiple scanline optimizations in semi-global matching. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 739–755 (2018)
Žbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1), 2287–2318 (2016)
Sinha, S.N., Scharstein, D., Szeliski, R.: Efficient high-resolution stereo matching using local plane sweeps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1582–1589 (2014)
Chang, Q., Maruyama, T.: Real-time stereo vision system: a multi-block matching on GPU. IEEE Access 6, 42030–42046 (2018)
Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In :Computer Vision–ACCV 2010, pp. 25–38. Springer (2010)
Lee, J., Jun, D., Eem, C., Hong, H.: Improved census transform for noise robust stereo matching. Opt. Eng. 55(6), 063107 (2016)
Yann, L., Yoshua, B.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)
Matthew, J., Dustin, R., Matthew, H., Ryan, K.: RIFFA 2.1: a reusable integration framework for FPGA accelerators. ACM Trans. Reconfigur. Technol. Syst. 8(4), 22 (2015)
Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. In: Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pp. 467–474. IEEE (2011)
Robert, S., Tobias, ., Raúl, R.: Weighted semi-global matching and center-symmetric census transform for robust driver assistance. In: International Conference on Computer Analysis of Images and Patterns, pp. 34–41. Springer (2013)
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Cambuim, L.F.S., Oliveira, L.A., Barros, E.N.S. et al. An FPGA-based real-time occlusion robust stereo vision system using semi-global matching. J Real-Time Image Proc 17, 1447–1468 (2020). https://doi.org/10.1007/s11554-019-00902-w
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DOI: https://doi.org/10.1007/s11554-019-00902-w