A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

Item

Title
en_US A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference
Creator
en_US Grauman, Kristen
Date
2004-10-20T20:31:53Z
Date Available
2004-10-20T20:31:53Z
Date Issued
en_US 2003-05-22
Identifier
en_US AITR-2003-007
Abstract
en_US We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. 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 data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.
Extent
en_US 60 p.
14619811 bytes
42799632 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-2003-007
Subject
en_US AI
en_US visual hull
en_US 3D structure
en_US shape model
en_US Bayesian inference