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Carnegie Mellon Robotics Institute

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Abstract

This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children?s pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: ?ground?, ?sky?, and ?vertical?. These labels are then used to ?cut and fold? the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However, it performs surprisingly well for a wide range of scenes taken from a typical person?s photo album.

Keywords

3D model, geometry, vision, single image

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Project(s): Geometrically Coherent Image Interpretation

Number of pages: 8

Note: AVI Video available at: http://www.cs.cmu.edu/~dhoiem/projects/popup/popup_movie_912_500_DivX.avi

Text Reference

Derek Hoiem, Alexei A. Efros, and Martial Hebert, "Automatic Photo Pop-up," ACM SIGGRAPH, August, 2005.

BibTeX Reference

@inproceedings{Hoiem_2005_5125,
   author = "Derek Hoiem and Alexei A. Efros and Martial Hebert",
   title = "Automatic Photo Pop-up",
   booktitle = "ACM SIGGRAPH",
   month = "August",
   year = "2005",
   Notes = "AVI Video available at: http://www.cs.cmu.edu/~dhoiem/projects/popup/popup_movie_912_500_DivX.avi"
}

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