A Bayesian Approach to Image-Based Visual Hull Reconstruction

We present a Bayesian approach to image-based visual hull reconstruction. The 3-D shape of an object of a known class is represented by sets of silhouette views simultaneously observed from multiple cameras. 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. In our representation, 3-D information is implicit in the joint observations of multiple contours from known viewpoints. We model the prior density using a probabilistic principal components analysis-based technique and estimate a maximum a posteriori reconstruction of multi-view contours. The proposed method is applied to a dataset of pedestrian images, and improvements in the approximate 3-D models under various noise conditions are shown.

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Kristen Grauman, Gregory Shakhnarovich, Trevor Darrell
• Keywords: Approximate 3-D Models, Computer Vision, CVPR 2003, Image-based Visual Hull, Posteriori Reconstruction, Silhouette Extraction Process, Visual Hull Reconstruction