A Newton Method for Pose Reenement of 3d Models

Diierent optimization methods can be employed to otpimize a numerical estimate for the match between an instantiated object model and an image. In order to take advantage of gradient-based optimization methods, perspective inversion must be used in this context. We show that convergence can be very fast by extrapolating to maximum goodness-of-t with Newton's method. This approach is related to methods which either maximize a similar goodness-of-t measure without use of gradient information, or else minimize distances between projected model lines and image features. Newton's method combines the accuracy of the former approach with the speed of convergence of the latter.