Ahmed M, Haas C, Haas R (2011) Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach. Can J Civ Eng 38(12):1301–1313
Ansar A, Castano A, Matthies L (2004) Enhanced real-time stereo using bilateral filtering. In: Proceedings. 2nd international symposium on 3D data processing, visualization and transmission, 2004. 3DPVT 2004, pp 455–462. https://doi.org/10.1109/TDPVT.2004.1335273
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. https://doi.org/10.1109/34.121791
Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239
Brunken H, Gühmann C (2019) Incorporating plane-sweep in convolutional neural network stereo imaging for road surface reconstruction. In: Proceedings of the 14th international joint conference on computer vision, imaging and computer graphics theory and applications—volume 5: VISAPP, SciTePress, pp 784–791. https://doi.org/10.5220/0007352107840791
Bulatov D (2015) Temporal selection of images for a fast algorithm for depth-map extraction in multi-baseline configurations. In: Proceedings of the 10th international conference on computer vision theory and applications, SciTePress, Berlin, Germany, pp 395–402. https://doi.org/10.5220/0005239503950402. http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005239503950402
Bundesanstalt für Straßenwesen (2018) Measuring vehicles and systems. https://www.bast.de/durabast/DE/durabast/Messfahrzeuge/fahrzeuge_node.html. Accessed 10 Nov 2018
Chang JR, Chen YS (2018) Pyramid stereo matching network, p 9. arXiv:180308669
Coenen TBJ, Golroo A (2017) A review on automated pavement distress detection methods. Cogent Eng 4(1):1374822. https://doi.org/10.1080/23311916.2017.1374822
Collins RT (1996) A space-sweep approach to true multi-image matching. In: Proceedings CVPR IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 358–363. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=517097
Devernay F (1994) Computing differential properties of 3-D shapes from stereoscopic images without 3-D models. In: Proceedings of IEEE conference on computer vision and pattern recognition CVPR-94, IEEE Computer Society Press, Seattle, WA, USA, pp 208–213. https://doi.org/10.1109/CVPR.1994.323831. http://ieeexplore.ieee.org/document/323831/
Eisenbach M, Stricker R, Seichter D, Amende K, Debes K, Sesselmann M, Ebersbach D, Stoeckert U, Gross HM (2017) How to get pavement distress detection ready for deep learning? A systematic approach. In: 2017 international joint conference on neural networks (IJCNN), IEEE, Anchorage, AK, USA, pp 2039–2047. https://doi.org/10.1109/IJCNN.2017.7966101. http://ieeexplore.ieee.org/document/7966101/
El Gendy A, Shalaby A, Saleh M, Flintsch GW (2011) Stereo-vision applications to reconstruct the 3D texture of pavement surface. Int J Pavement Eng 12(3):263–273. https://doi.org/10.1080/10298436.2010.546858
Fan R, Ai X, Dahnoun N (2018) Road surface 3D reconstruction based on dense subpixel disparity map estimation. IEEE Trans Image Process 27(6):3025–3035. https://doi.org/10.1109/TIP.2018.2808770
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Gallup D, Frahm JM, Mordohai P, Yang Q, Pollefeys M (2007) Real-time plane-sweeping stereo with multiple sweeping directions. In: 2007 IEEE conference on computer vision and pattern recognition, IEEE, pp 1–8. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4270270
Gang L, Zucker S (2006) Surface geometric constraints for stereo in belief propagation. In: 2006 IEEE computer society conference on computer vision and pattern recognition—volume 2 (CVPR’06), IEEE, New York, NY, USA, pp 2355–2362. https://doi.org/10.1109/CVPR.2006.299. http://ieeexplore.ieee.org/document/1641042/. Accessed 30 April 2020
GPL software (2017) Cloudcompare 2.8. http://www.cloudcompare.org. Accessed 01 Feb 2018
Hirschmüller H (2008) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341. https://doi.org/10.1109/TPAMI.2007.1166
Hirschmüller H, Scharstein D (2009) Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans Pattern Anal Mach Intell 31(9):1582–1599. https://doi.org/10.1109/TPAMI.2008.221
Irschara A, Rumpler M, Meixner P, Pock T, Bischof H (2012) Efficient and globally optimal multi view dense matching for aerial images. In: ISPRS annals of photogrammetry, remote sensing and spatial information sciences, vol I-3, pp 227–232. https://doi.org/10.5194/isprsannals-I-3-227-2012. http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/227/2012/. Accessed 16 Oct 2018
Ivanchenko V, Shen H, Coughlan J (2009) Elevation-based MRF stereo implemented in real-time on a GPU. In: 2009 workshop on applications of computer vision (WACV), IEEE, pp 1–8.
Kim J, Kolmogorov V, Zabih R (2003) Visual correspondence using energy minimization and mutual information. In: Proceedings ninth IEEE international conference on computer vision, IEEE, vol 2, pp 1033–1040. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1238463. Accessed 18 Aug 2016
Li L, Zhang S, Yu X, Zhang L (2018) PMSC: PatchMatch-based superpixel cut for accurate stereo matching. IEEE Trans Circuits Syst Video Technol 28(3):679–692
Olsson C, Ulén J, Boykov Y (2013) In defense of 3D-label stereo. In: 2013 IEEE conference on computer vision and pattern recognition, pp 1730–1737. https://doi.org/10.1109/CVPR.2013.226
Roth L, Mayer H (2019) Reduction of the fronto-parallel bias for wide-baseline semi-global matching. In: ISPRS annals of photogrammetry, remote sensing and spatial information sciences, vol IV-2/W5, pp 69–76. https://doi.org/10.5194/isprs-annals-IV-2-W5-69-2019. https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W5/69/2019/. Accessed 23 Dec 2019
Scharstein D, Taniai T, Sinha SN (2017) Semi-global stereo matching with surface orientation priors. In: 2017 international conference on 3D vision (3DV), IEEE, Qingdao, pp 215–224. https://doi.org/10.1109/3DV.2017.00033. https://ieeexplore.ieee.org/document/8374574/. Accessed 16 Oct 2018
Sinha SN, Scharstein D, Szeliski R (2014) Efficient high-resolution stereo matching using local plane sweeps. In: 2014 IEEE conference on computer vision and pattern recognition, pp 1582–1589. https://doi.org/10.1109/CVPR.2014.205
Szeliski R (2011) Computer vision. Texts in computer science. Springer, London. http://link.springer.com/10.1007/978-1-84882-935-0
Szeliski R, Zabih R, Scharstein D, Veksler O, Kolmogorov V, Agarwala A, Tappen M, Rother C (2008) A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans Pattern Anal Mach Intell 30(6):1068–1080. https://doi.org/10.1109/TPAMI.2007.70844
Wang KC, Gong W (2002) Automated pavement distress survey: a review and a new direction. In: Pavement evaluation conference, pp 21–25
Woodford O, Torr P, Reid I, Fitzgibbon A (2009) Global stereo reconstruction under second-order smoothness priors. IEEE Trans Pattern Anal Mach Intell 31(12):2115–2128. https://doi.org/10.1109/TPAMI.2009.131
Yang R, Welch G, Bishop G (2002) Real-time consensus-based scene reconstruction using commodity graphics hardware. In: 10th Pacific conference on computer graphics and applications, 2002. Proceedings., vol 22, pp 225–234
Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. European conference on computer vision. Springer, Berlin, pp 151–158
Zbontar J, LeCun Y (2016) Stereo matching by training a convolutional neural network to compare image patches. J Mach Learn Res 17(1–32):2
Zhang Z, Ai X, Chan CK, Dahnoun N (2014) An efficient algorithm for pothole detection using stereo vision. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, Florence, Italy, pp 564–568. https://doi.org/10.1109/ICASSP.2014.6853659. http://ieeexplore.ieee.org/document/6853659/. Accessed 16 Oct 2018
Zhao W, Yan L, Zhang Y (2018) Geometric-constrained multi-view image matching method based on semi-global optimization. Geo-spat Inf Sci 21(2):115–126. https://doi.org/10.1080/10095020.2018.1441754