Probabilistic account for multi-view stereo
published: Feb. 25, 2007, recorded: May 2004, views: 116
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This paper describes a method for dense depth reconstruction from wide-baseline images. In a wide-baseline setting an inherent difficulty which complicates the stereo correspondence problem is self-occlusion. Also, we have to consider the possibility that image pixels in different images, which are projections of the same point in the scene, will have different colour values due to non-Lambertian effects or discretization errors. We propose a Bayesian approach to tackle these problems. In this framework, the images are regarded as noisy measurements of an underlying 'true' image-function. Also, the image data is considered incomplete, in the sense that we do not know which pixels from a particular image are occluded in the other images. We describe an EM-algorithm, which iterates between estimating values for all hidden quantities, and optimising the current depth estimates. The algorithm has few free parameters, displays a stable convergence behaviour and generates accurate depth estimates.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !