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Automatic Single-Image 3d Reconstructions of Indoor Manhattan World Scenes

  • Erick Delage
  • Honglak Lee
  • Andrew Y. Ng
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 28)

Abstract

3d reconstruction from a single image is inherently an ambiguous problem. Yet when we look at a picture, we can often infer 3d information about the scene. Humans perform single-image 3d reconstructions by using a variety of single-image depth cues, for example, by recognizing objects and surfaces, and reasoning about how these surfaces are connected to each other. In this paper, we focus on the problem of automatic 3d reconstruction of indoor scenes, specifically ones (sometimes called “Manhattan worlds”) that consist mainly of orthogonal planes. We use a Markov random field (MRF) model to identify the different planes and edges in the scene, as well as their orientations. Then, an iterative optimization algorithm is applied to infer the most probable position of all the planes, and thereby obtain a 3d reconstruction. Our approach is fully automatic—given an input image, no human intervention is necessary to obtain an approximate 3d reconstruction.

Keywords

Markov Random Field Dynamic Bayesian Network Edge Orientation Indoor Scene Markov Random Field Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Erick Delage
    • 1
  • Honglak Lee
    • 1
  • Andrew Y. Ng
    • 1
  1. 1.Stanford UniversityStanfordUSA

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