“Computational zoom: a framework for post-capture image composition” by Badki, Gallo, Kautz and Sen

  • ©Abhishek Badki, Orazio Gallo, Jan Kautz, and Pradeep Sen



Session Title:

    Get More Out of Your Photo


    Computational zoom: a framework for post-capture image composition




    Capturing a picture that “tells a story” requires the ability to create the right composition. The two most important parameters controlling composition are the camera position and the focal length of the lens. The traditional paradigm is for a photographer to mentally visualize the desired picture, select the capture parameters to produce it, and finally take the photograph, thus committing to a particular composition. We propose to change this paradigm. To do this, we introduce computational zoom, a framework that allows a photographer to manipulate several aspects of composition in post-processing from a stack of pictures captured at different distances from the scene. We further define a multi-perspective camera model that can generate compositions that are not physically attainable, thus extending the photographer’s control over factors such as the relative size of objects at different depths and the sense of depth of the picture. We show several applications and results of the proposed computational zoom framework.


    1. Aseem Agarwala, Maneesh Agrawala, Michael Cohen, David Salesin, and Richard Szeliski. 2006. Photographing Long Scenes with Multi-viewpoint Panoramas. ACM Trans. Graph. 25, 3 (2006), 853–861. Google ScholarDigital Library
    2. Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman. 2009. Patch-Match: A Randomized Correspondence Algorithm for Structural Image Editing. ACM Trans. Graph. 28, 3, Article 24 (2009), 24:1–24:11 pages.Google ScholarDigital Library
    3. Michael Bleyer, Christoph Rhemann, and Carsten Rother. 2011. PatchMatch Stereo-Stereo Matching with Slanted Support Windows. In BMVC. 14.1–14.11. Google ScholarCross Ref
    4. Yuri Boykov and Vladimir Kolmogorov. 2004. An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision. IEEE Trans. Pattern Anal. Mach. Intell. 26, 9 (2004), 1124–1137. Google ScholarDigital Library
    5. Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. 2001. Unstructured Lumigraph Rendering. In Proceedings SIGGRAPH. 425–432. Google ScholarDigital Library
    6. Robert Carroll, Aseem Agarwala, and Maneesh Agrawala. 2010. Image Warps for Artistic Perspective Manipulation. ACM Trans. Graph. 29, 4, Article 127 (2010), 9 pages.Google ScholarDigital Library
    7. Gaurav Chaurasia, Sylvain Duchene, Olga Sorkine-Hornung, and George Drettakis. 2013. Depth Synthesis and Local Warps for Plausible Image-based Navigation. ACM Trans. Graph. 32, 3, Article 30 (2013), 12 pages.Google ScholarDigital Library
    8. Gaurav Chaurasia, OlgaSorkine, and George Drettakis. 2011. Silhouette-aware Warping for Image-based Rendering. In EGSR. 1223–1232.Google Scholar
    9. Jiawen Chen, S. Paris, J. Wang, W. Matusik, M. Cohen, and F. Durand. 2011. The Video Mesh: A Data Structure for Image-based Three-dimensional Video Editing. In IEEE ICCP. 1–8.Google Scholar
    10. Shenchang Eric Chen and Lance Williams. 1993. View Interpolation for Image Synthesis. In Proceedings SIGGRAPH. 279–288. Google ScholarDigital Library
    11. Paul E. Debevec, Camillo J. Taylor, and Jitendra Malik. 1996. Modeling and Rendering Architecture from Photographs: A Hybrid Geometry- and Image-based Approach. In Proceedings SIGGRAPH. 11–20. Google ScholarDigital Library
    12. Ohad Fried, Eli Shechtman, Dan B. Goldman, and Adam Finkelstein. 2016. Perspective-aware Manipulation of Portrait Photos. ACM Trans. Graph. 35, 4, Article 128 (2016), 10 pages.Google ScholarDigital Library
    13. Simon Fuhrmann, Fabian Langguth, and Michael Goesele. 2014. MVE: A Multi-view Reconstruction Environment. In Eurographics Workshop on Graphics and Cultural Heritage. 11–18.Google Scholar
    14. Yasutaka Furukawa and Jean Ponce. 2010. Accurate, Dense, and Robust Multiview Stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 8 (2010), 1362–1376. Google ScholarDigital Library
    15. Silvano Galliani, Katrin Lasinger, and Konrad Schindler. 2015. Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. In IEEE CVPR. 873–881. Google ScholarDigital Library
    16. Orazio Gallo, Alejandro Troccoli, Jun Hu, Kari Pulli, and Jan Kautz. 2015. Locally Non-rigid Registration for Mobile HDR Photography. In IEEE CVPR Workshops. 49–56. Google ScholarCross Ref
    17. Steven J. Gortler, Radek Grzeszczuk, Richard Szeliski, and Michael F. Cohen. 1996. The Lumigraph. In Proceedings SIGGRAPH. 43–54. Google ScholarDigital Library
    18. K. He, C. Rhemann, C. Rother, X. Tang, and J. Sun. 2011. A Global Sampling Method for Alpha Matting. In IEEE CVPR. 2049–2056. Google ScholarDigital Library
    19. Philipp Heise, Brian Jensen, Sebastian Klose, and Alois Knoll. 2015. Variational Patch-Match MultiView Reconstruction and Refinement. In IEEE CVPR. 882–890.Google Scholar
    20. Felix Klose, Oliver Wang, Jean-Charles Bazin, Marcus Magnor, and Alexander Sorkine-Hornung. 2015. Sampling Based Scene-space Video Processing. ACM Trans. Graph. 34, 4, Article 67 (2015), 11 pages.Google ScholarDigital Library
    21. Johannes Kopf, Billy Chen, Richard Szeliski, and Michael Cohen. 2010. Street Slide: Browsing Street Level Imagery. ACM Trans. Graph. 29, 4, Article 96 (2010), 8 pages.Google ScholarDigital Library
    22. Johannes Kopf, Michael F. Cohen, Dani Lischinski, and Matt Uyttendaele. 2007. Joint Bilateral Upsampling. ACM Trans. Graph. 26, 3, Article 96 (2007). Google ScholarDigital Library
    23. Johannes Kopf, Michael F. Cohen, and Richard Szeliski. 2014. First-person Hyper-lapse Videos. ACM Trans. Graph. 33, 4, Article 78 (2014), 10 pages.Google ScholarDigital Library
    24. Johannes Kopf, Fabian Langguth, Daniel Scharstein, Richard Szeliski, and Michael Goesele. 2013. Image-based Rendering in the Gradient Domain. ACM Trans. Graph. 32, 6, Article 199 (2013), 9 pages.Google ScholarDigital Library
    25. Marc Levoy and Pat Hanrahan. 1996. Light Field Rendering. In Proceedings SIGGRAPH 31–42. Google ScholarDigital Library
    26. Henrik Lieng, James Tompkin, and Jan Kautz. 2012. Interactive Multi-perspective Imagery from Photos and Videos. Comput. Graph. Forum 31, 2pt1 (2012), 285–293. Google ScholarDigital Library
    27. Voicu Popescu, Paul Rosen, and Nicoletta Adamo-Villani. 2009. The Graph Camera. ACM Trans. Graph. 28, 5, Article 158 (2009), 8 pages.Google ScholarDigital Library
    28. Augusto Roman, Gaurav Garg, and Marc Levoy. 2004. Interactive Design of Multi-Perspective Images for Visualizing Urban Landscapes. In Proc. Conference on Visualization. 537–544. Google ScholarDigital Library
    29. Steven M. Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In IEEE CVPR. 519–528. Google ScholarDigital Library
    30. Steven M. Seitz and Jiwon Kim. 2003. Multiperspective Imaging. IEEE Comput. Graph. Appl. 23, 6 (2003), 16–19. Google ScholarDigital Library
    31. Heung-Yeung Shum, Shing-Chow Chan, and Sing Bing Kang. 2007. Image-Based Rendering. Springer.Google Scholar
    32. Sudipta N. Sinha, Johannes Kopf, Michael Goesele, Daniel Scharstein, and Richard Szeliski. 2012. Image-based Rendering for Scenes with Reflections. ACM Trans. Graph. 31, 4, Article 100 (2012), 10 pages.Google ScholarDigital Library
    33. Changchang Wu. 2011. VisualSFM : A Visual Structure from Motion System. http://ccwu.me/vsfm/Google Scholar
    34. C. Wu, S. Agarwal, B. Curless, and S. M. Seitz. 2011. Multicore Bundle Adjustment. In IEEE CVPR. 3057–3064. Google ScholarDigital Library
    35. J. Yu and L. McMillan. 2004. A Framework for Multiperspective Rendering. In EGSR. 61–68.Google Scholar
    36. Jingyi Yu, Leonard McMillan, and Peter Sturm. 2008. Multiperspective Modeling, Rendering, and Imaging. In ACM SIGGRAPH ASIA 2008 Courses. Article 14, 36 pages.Google ScholarDigital Library
    37. A. Zomet, D. Feldman, S. Peleg, and D. Weinshall. 2003. Mosaicing New Views: The Crossed-Slits Projection. IEEE Trans. Pattern Anal. Mach. Intell. 25, 6 (June 2003), 741–754. Google ScholarDigital Library

ACM Digital Library Publication: