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Inferring 3D Structure with a Statistical Image-Based Shape Model

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dc.creator Grauman, Kristen
dc.creator Shakhnarovich, Gregory
dc.creator Darrell, Trevor
dc.date 2004-10-08T20:38:51Z
dc.date 2004-10-08T20:38:51Z
dc.date 2003-04-17
dc.date.accessioned 2013-10-09T02:46:32Z
dc.date.available 2013-10-09T02:46:32Z
dc.date.issued 2013-10-09
dc.identifier AIM-2003-008
dc.identifier http://hdl.handle.net/1721.1/6714
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.
dc.format 17 p.
dc.format 6362014 bytes
dc.format 9371703 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-2003-008
dc.subject AI
dc.subject 3D structure
dc.subject statistical shape model
dc.subject multi-view imagery
dc.subject pose estimation
dc.title Inferring 3D Structure with a Statistical Image-Based Shape Model


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