A statistically-based Newton method for pose refinement

摘要

Given a measure for the match of an instantiated model line to an image, it is possible to minimize the probability of obtaining an accidental match by descending the gradient of the ‘line energy’ (log-probability). By projecting this gradient onto parameter space, Newton's method can be applied to recovering the pose parameters of 3D models that minimize the probability of an accidental match between instantiated model and image.