Abstract
In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke. Despite the high complexity of RS geometry, we focus in this paper on a special yet common input—two consecutive frames from a video stream, wherein the inter-frame motion is restricted from being arbitrarily large. This allows us to adopt simpler differential motion model, leading to a straightforward and practical minimal solver. To deal with non-planar scene and camera parallax in stitching, we further propose an RS-aware spatially-varying homogarphy field in the principle of As-Projective-As-Possible (APAP). We show superior performance over state-of-the-art methods both in RS image stitching and rectification, especially for images captured by hand-held shaking cameras.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
See our supplementary material for derivations.
References
Albl, C., Kukelova, Z., Larsson, V., Polic, M., Pajdla, T., Schindler, K.: From two rolling shutters to one global shutter. In: CVPR (2020)
Albl, C., Kukelova, Z., Pajdla, T.: R6p-rolling shutter absolute camera pose. In: CVPR (2015)
Albl, C., Kukelova, Z., Pajdla, T.: Rolling shutter absolute pose problem with known vertical direction. In: CVPR (2016)
Albl, C., Sugimoto, A., Pajdla, T.: Degeneracies in rolling shutter SfM. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 36–51. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_3
Bapat, A., Price, T., Frahm, J.M.: Rolling shutter and radial distortion are features for high frame rate multi-camera tracking. In: CVPR (2018)
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)
Chang, C.H., Sato, Y., Chuang, Y.Y.: Shape-preserving half-projective warps for image stitching. In: CVPR (2014)
Chen, Y.S., Chuang, Y.Y.: Natural image stitching with the global similarity prior. In: ECCV (2016)
Cox, D.A., Little, J., O’shea, D.: Using Algebraic Geometry, vol. 185. Springer, Heidelberg (2006). https://doi.org/10.1007/b138611
Dai, Y., Li, H., Kneip, L.: Rolling shutter camera relative pose: generalized epipolar geometry. In: CVPR (2016)
Grundmann, M., Kwatra, V., Castro, D., Essa, I.: Calibration-free rolling shutter removal. In: ICCP (2012)
Haresh, S., Kumar, S., Zia, M.Z., Tran, Q.H.: Towards anomaly detection in dashcam videos. In: IV (2020)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Hartley, R.I.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)
Hedborg, J., Forssén, P.E., Felsberg, M., Ringaby, E.: Rolling shutter bundle adjustment. In: CVPR (2012)
Heeger, D.J., Jepson, A.D.: Subspace methods for recovering rigid motion I: algorithm and implementation. Int. J. Comput. Vis. 7(2), 95–117 (1992)
Herrmann, C., et al.: Robust image stitching with multiple registrations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 53–69. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_4
Herrmann, C., Wang, C., Bowen, R.S., Keyder, E., Zabih, R.: Object-centered image stitching. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 846–861. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_50
Horn, B.K.: Motion fields are hardly ever ambiguous. Int. J. Comput. Vis. 1(3), 259–274 (1988)
Im, S., Ha, H., Choe, G., Jeon, H.G., Joo, K., So Kweon, I.: High quality structure from small motion for rolling shutter cameras. In: ICCV (2015)
Ito, E., Okatani, T.: Self-calibration-based approach to critical motion sequences of rolling-shutter structure from motion. In: CVPR (2017)
Klingner, B., Martin, D., Roseborough, J.: Street view motion-from-structure-from-motion. In: ICCV (2013)
Kukelova, Z., Albl, C., Sugimoto, A., Pajdla, T.: Linear solution to the minimal absolute pose rolling shutter problem. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 265–280. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_17
Lao, Y., Aider, O.A.: Rolling shutter homography and its applications. In: IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Lao, Y., Ait-Aider, O.: A robust method for strong rolling shutter effects correction using lines with automatic feature selection. In: CVPR (2018)
Lao, Y., Ait-Aider, O., Bartoli, A.: Rolling shutter pose and ego-motion estimation using shape-from-template. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 477–492. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_29
Lee, K.Y., Sim, J.Y.: Warping residual based image stitching for large parallax. In: CVPR (2020)
Li, S., Yuan, L., Sun, J., Quan, L.: Dual-feature warping-based motion model estimation. In: ICCV (2015)
Liao, T., Li, N.: Single-perspective warps in natural image stitching. IEEE Trans. Image Process. 29, 724–735 (2019)
Lin, C.C., Pankanti, S.U., Natesan Ramamurthy, K., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: CVPR (2015)
Lin, K., Jiang, N., Cheong, L.-F., Do, M., Lu, J.: SEAGULL: seam-guided local alignment for parallax-tolerant image stitching. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 370–385. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_23
Lin, K., Jiang, N., Liu, S., Cheong, L.F., Do, M., Lu, J.: Direct photometric alignment by mesh deformation. In: CVPR (2017)
Lin, W.Y., Liu, S., Matsushita, Y., Ng, T.T., Cheong, L.F.: Smoothly varying affine stitching. In: CVPR (2011)
Liu, F., Gleicher, M., Jin, H., Agarwala, A.: Content-preserving warps for 3D video stabilization. ACM Trans. Graph. (TOG) 28(3), 1–9 (2009)
Liu, P., Cui, Z., Larsson, V., Pollefeys, M.: Deep shutter unrolling network. In: CVPR (2020)
Liu, S., Yuan, L., Tan, P., Sun, J.: Bundled camera paths for video stabilization. ACM Trans. Graph. (TOG) 32(4), 1–10 (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Ma, Y., Košecká, J., Sastry, S.: Linear differential algorithm for motion recovery: a geometric approach. Int. J. Comput. Vis. 36(1), 71–89 (2000)
Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models, vol. 26. Springer, Heidelberg (2012)
Magerand, L., Bartoli, A., Ait-Aider, O., Pizarro, D.: Global optimization of object pose and motion from a single rolling shutter image with automatic 2D-3D matching. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 456–469. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_33
Meingast, M., Geyer, C., Sastry, S.: Geometric models of rolling-shutter cameras. In: Workshop on Omnidirectional Vision, Camera Networks and Non-Classical Cameras (2005)
Mohan, M.M., Rajagopalan, A., Seetharaman, G.: Going unconstrained with rolling shutter deblurring. In: ICCV (2017)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)
Muratov, O., Slynko, Y., Chernov, V., Lyubimtseva, M., Shamsuarov, A., Bucha, V.: 3DCapture: 3D reconstruction for a smartphone. In: CVPRW (2016)
Oth, L., Furgale, P., Kneip, L., Siegwart, R.: Rolling shutter camera calibration. In: CVPR (2013)
Punnappurath, A., Rengarajan, V., Rajagopalan, A.: Rolling shutter super-resolution. In: ICCV (2015)
Purkait, P., Zach, C.: Minimal solvers for monocular rolling shutter compensation under ackermann motion. In: WACV (2018)
Purkait, P., Zach, C., Leonardis, A.: Rolling shutter correction in Manhattan world. In: ICCV (2017)
Rengarajan, V., Balaji, Y., Rajagopalan, A.: Unrolling the shutter: CNN to correct motion distortions. In: CVPR (2017)
Rengarajan, V., Rajagopalan, A.N., Aravind, R.: From bows to arrows: rolling shutter rectification of urban scenes. In: CVPR (2016)
Rengarajan, V., Rajagopalan, A.N., Aravind, R., Seetharaman, G.: Image registration and change detection under rolling shutter motion blur. IEEE Trans. Pattern Anal. Mach. Intell. 39(10), 1959–1972 (2016)
Ringaby, E., Forssén, P.E.: Efficient video rectification and stabilisation for cell-phones. Int. J. Comput. Vis. 96(3), 335–352 (2012)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: ICCV (2011)
Saurer, O., Koser, K., Bouguet, J.Y., Pollefeys, M.: Rolling shutter stereo. In: ICCV (2013)
Saurer, O., Pollefeys, M., Hee Lee, G.: Sparse to dense 3D reconstruction from rolling shutter images. In: CVPR (2016)
Saurer, O., Pollefeys, M., Lee, G.H.: A minimal solution to the rolling shutter pose estimation problem. In: IROS (2015)
Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)
Schubert, D., Demmel, N., Usenko, V., Stuckler, J., Cremers, D.: Direct sparse odometry with rolling shutter. In: ECCV (2018)
Szeliski, R., et al.: Image alignment and stitching: a tutorial. Found. Trends® Comput. Graph. Vis. 2(1), 1–104 (2007)
Tran, Q.-H., Chin, T.-J., Carneiro, G., Brown, M.S., Suter, D.: In defence of RANSAC for outlier rejection in deformable registration. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 274–287. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_20
Vasu, S., Mohan, M.M., Rajagopalan, A.: Occlusion-aware rolling shutter rectification of 3D scenes. In: CVPR (2018)
Vasu, S., Rajagopalan, A.N., Seetharaman, G.: Camera shutter-independent registration and rectification. IEEE Trans. Image Process. 27(4), 1901–1913 (2017)
Zaragoza, J., Chin, T.J., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving DLT. In: CVPR (2013)
Zaragoza, J., Chin, T.J., Tran, Q.H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving DLT. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1285–1298 (2014)
Zhang, F., Liu, F.: Parallax-tolerant image stitching. In: CVPR (2014)
Zhuang, B., Cheong, L.F., Hee Lee, G.: Rolling-shutter-aware differential SFM and image rectification. In: ICCV (2017)
Zhuang, B., Cheong, L.F., Hee Lee, G.: Baseline desensitizing in translation averaging. In: CVPR (2018)
Zhuang, B., Tran, Q.H., Ji, P., Cheong, L.F., Chandraker, M.: Learning structure-and-motion-aware rolling shutter correction. In: CVPR (2019)
Zhuang, B., Tran, Q.H., Lee, G.H., Cheong, L.F., Chandraker, M.: Degeneracy in self-calibration revisited and a deep learning solution for uncalibrated SLAM. In: IROS (2019)
Acknowledgement
We would like to thank Buyu Liu, Gaurav Sharma, Samuel Schulter, and Manmohan Chandraker for proofreading and support of this work. We are also grateful to all the reviewers for their constructive suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1 (mp4 25969 KB)
Supplementary material 2 (mp4 33927 KB)
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhuang, B., Tran, QH. (2020). Image Stitching and Rectification for Hand-Held Cameras. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-58571-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58570-9
Online ISBN: 978-3-030-58571-6
eBook Packages: Computer ScienceComputer Science (R0)