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On Applications of Pyramid Doubly Joint Bilateral Filtering in Dense Disparity Propagation

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Abstract

Stereopsis is the basis for numerous tasks in machine vision, robotics, and 3D data acquisition and processing. In order for the subsequent algorithms to function properly, it is important that an affordable method exists that, given a pair of images taken by two cameras, can produce a representation of disparity or depth. This topic has been an active research field since the early days of work on image processing problems and rich literature is available on the topic. Joint bilateral filters have been recently proposed as a more affordable alternative to anisotropic diffusion. This class of image operators utilizes correlation in multiple modalities for purposes such as interpolation and upscaling. In this work, we develop the application of bilateral filtering for converting a large set of sparse disparity measurements into a dense disparity map. This paper develops novel methods for utilizing bilateral filters in joint, pyramid, and doubly joint settings, for purposes including missing value estimation and upscaling. We utilize images of natural and man-made scenes in order to exhibit the possibilities offered through the use of pyramid doubly joint bilateral filtering for stereopsis.

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  1. (UrixBlog, Stereoscopic photographs from all over the world [RU,EN], http://urixblog.com/

References

  1. Alvarez, L., Deriche, R., Sanchez, J., & Weickert, J. (2002). Dense disparity map estimation respecting image derivatives: A PDE and scale-space based approach. Journal of Visual Communication and Image Representation, 13(1/2), 3–21.

    Article  Google Scholar 

  2. Ansar, A., Castano, A., & Matthies, L. (2004). Enhanced real-time stereo using bilateral filtering. In: 2nd International symposium on 3D data processing, visualization and transmission (3DPVT 2004) (pp. 455–462).

  3. Arnold, D. (1978, May). Local context in matching edges for stereo vision. In: Image understanding workshop, Cambridge, MA (pp. 65–72).

  4. Arnold, R. D. (1983). Automated stereo perception. Tech. Rep. Technical Report AIM-351, Artificial Intelligence Laboratory, Stanford University.

  5. Ayache, N., & Faverjon, B. (1987). Efficient registration of stereo images by matching graph descriptions of edge segments. International Journal of Computer Vision, 1(2), 107–131.

    Article  Google Scholar 

  6. Baker, H. H., & Binford, T. O. (1981). Depth from edge and intensity based stereo. In: International joint conference on artificial intelligence (IJCAI 1981), Vancouver, BC, Canada (pp. 631–636).

  7. Barash, D. (2000). Bilateral filtering and anisotropic diffusion: towards a unified viewpoint. In: Tech. Rep. HPL-2000-18 (R.1), HP Laboratories Israel Technical Report, Technion City, Haifa 32000, Israel (August 2000).

  8. Barash, D. (2002). Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6), 844–847.

    Article  Google Scholar 

  9. Barnard, S. T. (1986). A stochastic approach to stereo vision. Tech. Rep. Technical Note 373, (April 1986). Menlo Park: SRI International.

  10. Barnard, S. T., & Fischler, M. A. (1982). Computational stereo. Computer Surveys, 14(4), 553–572.

    Google Scholar 

  11. Battiato, S., Cantone, D., Catalano, D., Cincotti, G., & Hofri, M. (2000). An efficient algorithm for the approximate median selection problem. In G. Bongiovanni, R. Petreschi, & G. Gambosi (Eds.), Algorithms and complexity. Lecture notes in computer science (Vol. 1767, pp. 226–238). Berlin: Springer.

  12. Bay, H., Tuytelaars, T., & Gool, L. V. (2006). Surf: Speeded up robust features. In: European conference on computer vision (ECCV 2006), Graz, Austria (pp. 404–417).

  13. Bignone, F., Henricsson, O., Fua, P., & Stricker, M. (1996). Automatic extraction of generic house roofs from high resolution aerial imagery. In: B. Buxton & R. Cipolla (Eds.), Proceedings of the European conference on computer vision (ECCV 1996) (Vol. 1064, pp. 83–96).

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

    Article  Google Scholar 

  15. Bolles, R. C., Baker, H. H., & Marimont, D. H. (1987). Epipolar-plane image analysis: An approach to determining structure from motion. International Journal of Computer Vision, 1, 7–55.

    Article  Google Scholar 

  16. Bolles, R. C., Baker, H. H., & Hannah, M. J. (1993). The JISCT stereo evaluation. In DARPA Image Understanding, Workshop (22nd, pp. 263–274).

  17. Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239.

    Article  Google Scholar 

  18. Brown, M. Z., Burschka, D., & Hager, G. D. (2003). Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), 993–1008.

  19. Buades, A., Coll, B., & Morel, J. (2005). A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation, 4(2), 490–530.

    Article  MATH  MathSciNet  Google Scholar 

  20. Chan, D., Buisman, H., Theobalt, C., & Thrun, S. (2008). A noise-aware filter for real-time depth upsampling. In: Workshop on multi-camera and multi-modal sensor fusion algorithms and applications (M2SFA2), Marseille, France.

  21. Chen, Q., & Medioni, G. (1999). A volumetric stereo matching method: Application to image-based modeling. In: IEEE Computer Society conference on computer vision and pattern recognition (CVPR 1999), Colorado (Vol. 1, pp. 29–34).

  22. Collins, R. T. (1996). A space-sweep approach to true multi-image matching. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR 1996), San Francisco (pp. 358–363).

  23. Dhond, U. R., & Aggarwal, J. (1989). Structure from stereo–a review. IEEE Transactions on Systems, Man and Cybernetics, 19(6), 1489–1510.

    Article  MathSciNet  Google Scholar 

  24. Dolson, J., Baek, J., Plagemann, C., & Thrun, S. (2010). Upsampling range data in dynamic environments. In: IEEE Conference on computer vision and pattern recognition (CVPR 2010) (pp. 1141–1148).

  25. Eisemann, E., & Durand, F. (2004). Flash photography enhancement via intrinsic relighting. ACM Transactions on Graphics, 23(3), 673–678.

    Article  Google Scholar 

  26. Elad, M. (2002). On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing, 11(10), 1141–1151.

    Article  MathSciNet  Google Scholar 

  27. Elias, R. (2007). Sparse view stereo matching. Pattern Recognition Letters, 28(13), 1667–1678.

    Article  Google Scholar 

  28. Faugeras, O., & Keriven, R. (1998). Complete dense stereovision using level set methods. In H. Burkhardt & B. Neumann (Eds.), Proceedings of the European conference on computer vision (ECCV 1998) (Vol. 1406, pp. 379–393). Lecture Notes in Computer Science. Berlin, Heidelberg: Springer.

  29. Furukawa, Y., & Ponce, J. (2010). Accurate, dense, and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1362–1376.

    Article  Google Scholar 

  30. Gangwal, O., & Berretty, R. P. (2009). Depth map post-processing for 3D-TV. In: Digest of technical papers international conference on consumer electronics (ICCE 2009) (pp. 1–2).

  31. Garcia, F., Mirbach, B., Ottersten, B., Grandidier, F., & Cuesta, A. (2010). Pixel weighted average strategy for depth sensor data fusion. In: 17th IEEE International conference on image processing (ICIP 2010) (pp. 2805–2808).

  32. Gauglitz, S., Hollerer, T., & Turk, M. (2011). Evaluation of interest point detectors and feature descriptors for visual tracking. International Journal of Computer Vision, 94(3), 1–26.

    Article  Google Scholar 

  33. Goesele, M., Snavely, N., Curless, B., Hoppe, H., & Seitz, S. (2007). Multi-view stereo for community photo collections. In: IEEE 11th International conference on computer vision (ICCV 2007) (pp. 1–8).

  34. Gong, M., & Yang, Y. H. (2005). Fast unambiguous stereo matching using reliability-based dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 998–1003.

    Article  Google Scholar 

  35. Grimson, W. E. L. (1980, January). A computer implementation of a theory of human stereo vision. A.I. Memo No. 565. Cambridge: Massachusetts Institute of Technology.

  36. Grimson, W. E. L. (1985). Computational experiments with a feature based stereo algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 7(1), 17–34.

    Article  Google Scholar 

  37. Habbecke, M., & Kobbelt, L. (2007). A surface-growing approach to multi-view stereo reconstruction. In: IEEE Conference on computer vision and pattern recognition (CVPR 2007) (pp. 1–8).

  38. Hannah, M. J. (1974). Computer matching of areas in stereo images. Ph.D. thesis, Stanford University.

  39. Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In: 4th Alvey Vision Conference (pp. 147–151).

  40. He, K., Sun, J., & Tang, X. (2010). Guided image filtering. In: Proceedings of the 11th European conference on computer vision: Part I (ECCV 2010) (pp. 1–14).

  41. Hirschmuller, H. (2008). Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 328–341.

    Article  Google Scholar 

  42. Hornung, A., & Kobbelt, L. (2006). Robust and efficient photo-consistency estimation for volumetric 3d reconstruction. In: Proceedings of the 9th European conference on computer vision (ECCV 2006), Graz, Austria (Vol. II, pp. 179–190). Berlin: Springer.

  43. Hsieh, Y. C., McKeown, D., & Perlant, F. P. (1992). Performance evaluation of scene registration and stereo matching for cartographic feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 214–238.

    Article  Google Scholar 

  44. Huang, T. S., Yang, G. Y., & Tang, G. Y. (1979). A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech and Signal Processing, 27(1), 13–18.

    Article  Google Scholar 

  45. Integrating Vision Toolkit (IVT), version 1.3.20. http://ivt.sourceforge.net/. Accessed 31 March 2014.(February 2013)

  46. Izquierdo M., E., & Ghanbari, M. (1998). Texture smoothing and object segmentation using feature-adaptive weighted Gaussian filtering. In: SBT/IEEE International telecommunications symposium (ITS 1998) (Vol. 2, pp. 650–655).

  47. Kolmogorov, V., & Zabih, R. (2002). Multi-camera scene reconstruction via graph cuts. In: Proceedings of the 7th European conference on computer vision–part III (ECCV 2002), London, UK (pp. 82–96).

  48. Kopf, J., Cohen, M. F., Lischinski, D., & Uyttendaele, M. (2007). Joint bilateral upsampling. ACM Transactions on graphics (proceedings of SIGGRAPH 2007) (Vol. 26, No. 3) (July 2007).

  49. Kutulakos, K. N., & Seitz, S. M. (2000). A theory of shape by space carving. International Journal of Computer Vision, 38(3), 199–218.

    Article  MATH  Google Scholar 

  50. Lhuillier, M., & Quan, L. (1999). Image interpolation by joint view triangulation. In: IEEE Computer Society conference on computer vision and pattern recognition (CVPR 1999), Colorado (Vol. 2, pp. 139–145).

  51. Lhuillier, M., & Quan, L. (2002). Match propagation for image-based modeling and rendering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1140–1146.

    Article  Google Scholar 

  52. Lhuillier, M., & Quan, L. (2005). A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3), 418–433.

    Article  Google Scholar 

  53. Li, L., Zhang, C. M., & Yan, H. (2011). Cost aggregation strategy for stereo matching based on a generalized bilateral filter model. In R. Zhu, Y. Zhang, B. Liu, & C. Liu (Eds.), Information computing and applications, communications in computer and information science (Vol. 105, pp. 193–200). Berlin: Springer.

    Chapter  Google Scholar 

  54. Lim, H. S., & Binford, T. O. (1985, December 9–10). Stereo correspondence: Features and constraints. In: DARPA image understanding workshop, Miami Beach, FL (pp. 373–380).

  55. Louw, M., & Nicolls, F. (2007). Improved sparse correspondence resolution using loopy belief propagation with MRF clique based structure preservation. The seventh IASTED international conference on visualization, imaging and image processing, Palma de Mallorca, Spain (pp. 88–93).

  56. Louw, M., & Nicolls, F. (2010). Accelerated sparse feature correspondence resolution using loopy belief propagation with MRF clique based structure preservation. In: The eleventh IASTED international conference on computer graphics and imaging (CGIM 2010), Innsbruck, Austria (pp. 109–116) (February 2010).

  57. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In: IEEE international conference on computer vision (ICCV), Toronto, Canada (pp. 1150–1157).

  58. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  59. Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In: International joint conference on artificial intelligence (IJCAI 1981), Vancouver, BC, Canada (pp. 674–679).

  60. Maciel, J., & Costeira, J. P. (2003). A global solution to sparse correspondence problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), 187–199.

    Article  Google Scholar 

  61. Marcus, R. C., & Ward, W. C. (2013). DP: A fast median filter approximation. Tech. Rep. LA-UR-13-25331, Los Alamos National Laboratory.

  62. Marr, D., & Poggio, T. (1979). A computational theory of human stereo vision. Proceedings of Royal Society of London, B204, 301–328.

    Article  Google Scholar 

  63. Matthies, L., Kanade, T., & Szeliski, R. (1989). Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3(3), 209–236.

    Article  Google Scholar 

  64. Mattoccia, S., Giardino, S., & Gambini, A. (2010). Accurate and efficient cost aggregation strategy for stereo correspondence based on approximated joint bilateral filtering. The 9th Asian conference on computer vision–part II (ACCV 2009) (pp. 371–380). Berlin: Springer.

    Google Scholar 

  65. Mattoccia, S., Viti, M., & Ries, F. (2011). Near real-time fast bilateral stereo on the GPU. In: IEEE Computer Society conference on computer vision and pattern recognition workshops (CVPRW 2011) (pp. 136–143).

  66. Mayhew, J. E. W., & Frisby, J. P. (1980). The computation of binocular edges. Perception, 9, 69–87.

    Article  Google Scholar 

  67. Mayhew, J. E. W., & Frisby, J. P. (1981). Psychophysical and computational studies towards a theory of human stereopsis. Artificial Intelligence, 17, 349–385.

    Article  Google Scholar 

  68. Medioni, G., & Nevatia, R. (1985). Segment-based stereo matching. Computer Vision, Graphics, and Image Processing (CVGIP), 31(1), 2–18.

  69. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630.

    Article  Google Scholar 

  70. Mu, Y., Zhang, H., & Li, J. (2009). A global sparse stereo matching method under structure tensor constraint. In: International conference on information technology and computer science (ITCS 2009) (Vol. 1, pp. 609–612).

  71. Ohta, Y., & Kanade, T. (1985). Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-7(2), 139–154.

  72. Otto, G. P., & Chau, T. K. W. (1989). “Region-growing” algorithm for matching of terrain images. Image and Vision Computing, 7(2), 83–94.

    Article  Google Scholar 

  73. Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2009). Bilateral filtering: Theory and applications. Foundations and Trends in Computer Graphics and Vision, 4(1), 1–73.

    Article  Google Scholar 

  74. Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.

    Article  Google Scholar 

  75. Perreault, S., & Hebert, P. (2007). Median filtering in constant time. IEEE Transactions on Image Processing, 16(9), 2389–2394.

    Article  MathSciNet  Google Scholar 

  76. Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., & Toyama, K. (2004). Digital photography with flash and no-flash image pairs. ACM Transactions on Graphics, 23(3), 664–672.

    Article  Google Scholar 

  77. Pollard, S. B., Mayhew, J. W., & Frisby, J. P. (1985). PMF: A stereo correspondence algorithm using a disparity gradient limit. Perception, 14, 449–470.

    Article  Google Scholar 

  78. Ramanath, R., & Snyder, W. E. (2003). Adaptive demosaicking. Journal of Electronic Imaging, 12(4), 633–642.

    Article  Google Scholar 

  79. Riemens, A. K., Gangwal, O. P., Barenbrug, B., & Berretty, R. P. M. (2009). Multistep joint bilateral depth upsampling. In: Visual communications and image processing, San Jose, CA.

  80. Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. In: European conference on computer vision (ECCV 2006), Graz, Austria (pp. 430–443).

  81. Roy, S., & Cox, I. J. (1998). A maximum-flow formulation of the N-camera stereo correspondence problem. In: Proceedings of sixth international conference on computer vision, (ICCV 1998) (pp. 492–499).

  82. Saint-Marc, P., Chen, J. S., & Medioni, G. (1991). Adaptive smoothing: A general tool for early vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 514–529.

    Article  Google Scholar 

  83. Sarkis, M., & Diepold, K. (2008). Sparse stereo matching using belief propagation. In: 15th IEEE International conference on image processing (ICIP 2008) (pp. 1780–1783).

  84. Sarkis, M., & Diepold, K. (2008). Towards real-time stereo using non-uniform image sampling and sparse dynamic programming. In: The fourth international symposium on 3D data processing, visualization and transmission (3DPVT 2008), Atlanta, GA, USA (June 2008).

  85. Sawhney, H. S., Guo, Y., Hanna, K., Kumar, R., Adkins, S., & Zhou, S. (2001). Hybrid stereo camera: An IBR approach for synthesis of very high resolution stereoscopic image sequences. In: SIGGRAPH 2001, Los Angeles, California (pp. 451–460).

  86. Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1–3), 7–42.

    Article  MATH  Google Scholar 

  87. Schmid, C., & Zisserman, A. (2000). The geometry and matching of lines and curves over multiple views. International Journal of Computer Vision, 40(3), 199–233.

    Article  MATH  Google Scholar 

  88. Smith, B. M., Zhang, L., & Jin, H. (2009). Stereo matching with nonparametric smoothness priors in feature space. In: IEEE Conference on computer vision and pattern recognition (CVPR 2009) (pp. 485–492).

  89. Strecha, C., Tuytelaars, T., & Gool, L. V. (2003). Dense matching of multiple wide-baseline views. In: Proceedings of the ninth IEEE international conference on computer vision (ICCV 2003), Nice, France (Vol. 2, pp. 1194–1201).

  90. Strecha, C., Fransens, R., & Gool, L. V. (2006). Combined depth and outlier estimation in multi-view stereo. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 2394–2401.

    Google Scholar 

  91. Taylor, C. J. ((2003)) Surface reconstruction from feature based stereo. In: Ninth IEEE international conference on computer vision (ICCV 2003), Nice, France (Vol. 1, pp. 184–190).

  92. Terzopoulos, D. (1986). Regularization of inverse visual problems involving discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(4), 413–424.

  93. Tibshirani, R. J. (2008). Fast computation of the median by successive binning. ArXiv: 0806.3301.

  94. Tola, E., Lepetit, V., & Fua, P. (2008). A fast local descriptor for dense matching. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2008) (pp. 1–8).

  95. Tomasi, C., & Kanade, T. (1991). Detection and tracking of point features. In: Tech. Rep. CMU-CS-91-132. Pittsburgh: Carnegie Mellon University.

  96. Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. In: Sixth international conference on computer vision (ICCV 1998) (pp. 839–846).

  97. Tuytelaars, T., & Gool, L. V. (2000). Wide baseline stereo matching based on local, affinely invariant regions. In: Proceedings of the eleventh British machine vision conference (BMVC 2000), University of Bristol (pp. 412–422).

  98. Ulusoy, I., & Hancock, E. (2007). A statistical approach to sparse multi-scale phase-based stereo. Pattern Recognition, 40(9), 2504–2520.

    Article  MATH  Google Scholar 

  99. Varekamp, C., & Barenbrug, B. (2007). Improved depth propagation for 2D to 3D video conversion using key-frames. In: 4th European conference on visual media production (IETCVMP 2007) (pp. 1–7).

  100. Veksler, O. (2001). Semi-dense stereo correspondence with dense features. In: The 2001 IEEE Computer Society conference on computer vision and pattern recognition (CVPR 2001) (Vol. 2, pp. II490-II497).

  101. Venkateswar, V., & Chellappa, R. (1995). Hierarchical stereo and motion correspondence using feature groupings. International Journal of Computer Vision, 15(3), 245–269.

    Article  Google Scholar 

  102. Weiss, B. (2006). Fast median and bilateral filtering. ACM Transactions on Graphics, 25(3), 519–526.

    Article  Google Scholar 

  103. Xiao, C., Nie, Y., Hua, W., & Zheng, W. (2010). Fast multi-scale joint bilateral texture upsampling. The Visual Computer, 26(4), 263–275.

    Article  Google Scholar 

  104. Yao, J., & Cham, W. K. (2006). 3D modeling and rendering from multiple wide-baseline images by match propagation. Image Communication Signal Processing, 21(6), 506–518.

    Article  Google Scholar 

  105. Zhang, Z., & Shan, Y. (2000). A progressive scheme for stereo matching. In: Second European workshop on 3D structure from multiple images of large-scale environments (SMILE 2000), Dublin, Ireland (pp. 68–85).

  106. Zhang, Z., Deriche, R., Faugeras, O., & Luong, Q. T. (1995). A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78(1–2), 87–119.

    Article  Google Scholar 

  107. Zhao, G., Chen, L., & Chen, G. (2009). A speeded-up local descriptor for dense stereo matching. In: Proceedings of 16th IEEE international conference on image processing (ICIP 2009) (pp. 2101–2104).

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Acknowledgments

We acknowledge the reviews received on this paper from the respected anonymous referees. The author wishes to thank Sara Ayatollahzadeh and Seyed Mohsen Amiri for their help in locating critical pieces of literature needed for this work. We also acknowledge the generosity of Yury GolubinskyFootnote 1 for allowing us to use his photographic work to present the results of this work. The author thanks Mahsa Pezeshki for proofreading this manuscript.

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Abadpour, A. On Applications of Pyramid Doubly Joint Bilateral Filtering in Dense Disparity Propagation. 3D Res 5, 8 (2014). https://doi.org/10.1007/s13319-014-0008-5

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