“Time-lapse mining from internet photos” by Averbuch-Elor and Cohen-Or

  • ©Ricardo Martin Brualla, David Gallup, and Steven Seitz

Conference:


Type:


Title:

    Time-lapse mining from internet photos

Session/Category Title: Let’s Do the Time Warp

Presenter(s)/Author(s):


Moderator(s):


Abstract:


    We introduce an approach for synthesizing time-lapse videos of popular landmarks from large community photo collections. The approach is completely automated and leverages the vast quantity of photos available online. First, we cluster 86 million photos into landmarks and popular viewpoints. Then, we sort the photos by date and warp each photo onto a common viewpoint. Finally, we stabilize the appearance of the sequence to compensate for lighting effects and minimize flicker. Our resulting time-lapses show diverse changes in the world’s most popular sites, like glaciers shrinking, skyscrapers being constructed, and waterfalls changing course.

References:


    1. Abrams, A., Miskell, K., and Pless, R. 2013. The episolar constraint: Monocular shape from shadow correspondence. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 1407–1414. Google ScholarDigital Library
    2. Ackermann, J., Langguth, F., Fuhrmann, S., and Goesele, M. 2012. Photometric stereo for outdoor webcams. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 262–269. Google ScholarDigital Library
    3. Agarwal, S., Mierle, K., and Others. Ceres Solver. http://ceres-solver.org.Google Scholar
    4. Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., and Szeliski, R. 2011. Building rome in a day. Communications of the ACM 54, 10, 105–112. Google ScholarDigital Library
    5. Amirshahi, H., Kondo, S., Ito, K., and Aoki, T. 2008. An image completion algorithm using occlusion-free images from internet photo sharing sites. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E91-A, 10. Google ScholarDigital Library
    6. Apple. Apple iOS8 camera.Google Scholar
    7. Bae, S., Agarwala, A., and Durand, F. 2010. Computational rephotography. ACM Trans. Graph. 29, 3 (July), 24:1–24:15. Google ScholarDigital Library
    8. Bennett, E. P., and McMillan, L. 2007. Computational time-lapse video. In ACM SIGGRAPH 2007 Papers, ACM, New York, NY, USA, SIGGRAPH ’07. Google ScholarDigital Library
    9. Boykov, Y., Veksler, O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. Pattern Analysis and Machine Intelligence, IEEE Transactions on 23, 11, 1222–1239. Google ScholarDigital Library
    10. Extreme Ice Survey. http://extremeicesurvey.org.Google Scholar
    11. Hays, J., and Efros, A. A. 2007. Scene completion using millions of photographs. ACM Transactions on Graphics (SIGGRAPH 2007) 26, 3. Google ScholarDigital Library
    12. Hays, J., and Efros, A. A. 2008. im2gps: estimating geographic information from a single image. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).Google Scholar
    13. Instagram. Hyperlapse app.Google Scholar
    14. Jacobs, N., Bies, B., and Pless, R. 2010. Using cloud shadows to infer scene structure and camera calibration. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 1102–1109.Google Scholar
    15. Kang, S. B., and Szeliski, R. 2004. Extracting view-dependent depth maps from a collection of images. International Journal of Computer Vision 58, 139–163. Google ScholarDigital Library
    16. Karsch, K., Golparvar-Fard, M., and Forsyth, D. 2014. ConstructAide: Analyzing and visualizing construction sites through photographs and building models. ACM Trans. Graph. 33, 6 (November). Google ScholarDigital Library
    17. Kemelmacher-Shlizerman, I., Shechtman, E., Garg, R., and Seitz, S. M. 2011. Exploring photobios. In ACM SIGGRAPH 2011 Papers, SIGGRAPH ’11, 61:1–61:10. Google ScholarDigital Library
    18. Laffont, P.-Y., Bousseau, A., Paris, S., Durand, F., and Drettakis, G. 2012. Coherent intrinsic images from photo collections. ACM Transactions on Graphics (SIGGRAPH Asia Conference Proceedings) 31. Google ScholarDigital Library
    19. Laffont, P.-Y., Ren, Z., Tao, X., Qian, C., and Hays, J. 2014. Transient attributes for high-level understanding and editing of outdoor scenes. ACM Transactions on Graphics (proceedings of SIGGRAPH) 33, 4. Google ScholarDigital Library
    20. Matzen, K., and Snavely, N. 2014. Scene chronology. In Proc. European Conf. on Computer Vision.Google Scholar
    21. Rubinstein, M., Liu, C., Sand, P., Durand, F., and Freeman, W. T. 2011. Motion denoising with application to time-lapse photography. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’11, 313–320. Google ScholarDigital Library
    22. Schindler, G., and Dellaert, F. 2010. Probabilistic temporal inference on reconstructed 3d scenes. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 1410–1417.Google Scholar
    23. Schindler, G., Dellaert, F., and Kang, S. B. 2007. Inferring temporal order of images from 3d structure. In Computer Vision and Pattern Recognition, 2007. CVPR ’07. IEEE Conference on, 1–7.Google Scholar
    24. Shih, Y., Paris, S., Durand, F., and Freeman, W. T. 2013. Data-driven hallucination of different times of day from a single outdoor photo. ACM Trans. Graph. 32, 6 (Nov.), 200:1–200:11. Google ScholarDigital Library
    25. Simon, I., Snavely, N., and Seitz, S. M. 2007. Scene summarization for online image collections. ICCV 7, 1–8.Google Scholar
    26. Sunkavalli, K., Matusik, W., Pfister, H., and Rusinkiewicz, S. 2007. Factored time-lapse video. In ACM SIGGRAPH 2007 Papers, ACM, New York, NY, USA, SIGGRAPH ’07. Google ScholarDigital Library
    27. Taneja, A., Ballan, L., and Pollefeys, M. 2011. Image based detection of geometric changes in urban environments. In Computer Vision (ICCV), 2011 IEEE International Conference on, 2336–2343. Google ScholarDigital Library
    28. Taneja, A., Ballan, L., and Pollefeys, M. 2013. City-scale change detection in cadastral 3d models using images. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’13, 113–120. Google ScholarDigital Library
    29. Telea, A. 2004. An image inpainting technique based on the fast marching method. Journal of Graphics Tools 9, 1, 23–34.Google ScholarCross Ref
    30. Ulusoy, A. O., and Mundy, J. L. 2014. Image-based 4-d reconstruction using 3-d change detection. In Computer Vision–ECCV 2014. Springer, 31–45.Google Scholar
    31. Whyte, O., Sivic, J., and Zisserman, A. 2009. Get out of my picture! internet-based inpainting. In Proceedings of the 20th British Machine Vision Conference.Google Scholar
    32. Wu, C., 2011. VisualSFM: A visual structure from motion system. http://ccwu.me/vsfm.Google Scholar

ACM Digital Library Publication:


Overview Page: