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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (2006)
New York, NY
June 17, 2006 to June 22, 2006
ISSN: 1063-6919
ISBN: 0-7695-2597-0
pp: 2418-2428
Erick Delage , Stanford University
Honglak Lee , Stanford University
Andrew Y. Ng , Stanford University
ABSTRACT
When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.
INDEX TERMS
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CITATION

E. Delage, A. Y. Ng and H. Lee, "A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image," 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)(CVPR), New York, NY, 2006, pp. 2418-2428.
doi:10.1109/CVPR.2006.23
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