Abstract
From the earliest to the state-of-the-art algorithms, stereo depth estimation techniques often require a disparity search range (DSR) value to be chosen manually. However, the optimal DSR varies from one scene to another making the results depend on the operator input and operator having to optimize the configuration by using trial-and-error. In this paper we present a novel technique suitable for parallel computing architectures which detects the optimum DSR for a given scene without requiring operator input or prior knowledge of the scene. Experiments on stereo images from Middlebury, KITTI and Sceneflow bench-mark datasets indicate that our technique can automatically extract a suitable DSR value from different scenes, which leads to better consistency in matching. The technique presented here can be used with existing stereo algorithms to limit the size of the cost volume as it is being built (without requiring pre-processing or operator input). A CUDA based implementation of our method can deliver real-time performance on consumer grade GPUs at high frame rates.
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References
Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: GA-net: guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 185–194 (2019)
Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410–5418 (2018)
Cheng, X., Wang, P., Yang, R.: Learning depth with convolutional spatial propagation network. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6492, pp. 25–38. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19315-6_3
Zhang, Z., Shan, Y.: A progressive scheme for stereo matching. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A. (eds.) SMILE 2000. LNCS, vol. 2018, pp. 68–85. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45296-6_5
Kostková, J., Sara, R.: Automatic disparity search range estimation for stereo pairs of unknown scenes. In: Proceedings of the Computer Vision Winter Workshop, pp. 1–10 (2004)
Min, D., Yea, S., Arican, Z., Vetro, A.: Disparity search range estimation: enforcing temporal consistency. In: Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2366–2369. IEEE (2010)
Smirnov, S., Gotchev, A., Hannuksela, M.: A disparity range estimation technique for stereo-video streaming applications. In: Proceedings of 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–4. IEEE (2013)
Sizintsev, M., Wildes, R.P.: Coarse-to-fine stereo vision with accurate 3D boundaries. Image Vis. Comput. 28(3), 352–366 (2010)
Ozgunalp, U., Ai, X., Zhang, Z., Koc, G., Dahnoun, N.: Block-matching disparity map estimation using controlled search range. In: Proceedings of 2015 7th Computer Science and Electronic Engineering Conference (CEEC), pp. 35–40. IEEE (2015)
Ma, H., et al.: Multiple lane detection algorithm based on optimised dense disparity map estimation. In: Proceedings of 2018 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–5. IEEE (2018)
Mun, J.H., Ho, Y.S.: Guided image filtering based disparity range control in stereo vision. Electron. Imaging 2017(5), 130–136 (2017)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings of 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 1, pp. I–I. IEEE (2003)
Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11752-2_3
Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040–4048 (2016)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)
Hirschmüller, H., Buder, M., Ernst, I.: Memory efficient semi-global matching. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, pp. 371–376 (2012)
Zha, D., Jin, X., Xiang, T.: A real-time global stereo-matching on FPGA. Microprocess. Microsyst. 47, 419–428 (2016)
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Perera, R., Low, T. (2020). Automatic Stereo Disparity Search Range Detection on Parallel Computing Architectures. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_35
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DOI: https://doi.org/10.1007/978-3-030-50347-5_35
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