This paper describes a novel approach to the problem of recovering information from an image set by comparing the radiance of hypothesised point correspondences. This method is applicable to a number of problems in computer vision, but is explained particularly in terms of recovering geometry and camera parameters from image sets. The algorithm employs a cost-function to represent the probability that a hypothesised scene description and camera parameters generated the reference images and is characterised by its ability to execute on graphics hardware. Experiments show that minimisation of the cost-function converges to a valid solution provided there are adequate geometric constraints and projective coverage.
展开▼
