Detecting changes in 3-D shape using self-consistency

A method for reliably detecting change in the 3-D shape of objects that are well-modeled as single-value functions Z=f(x,y) is presented. It uses an estimate of the accuracy of the 3-D models derived from a set of images taken simultaneously. This accuracy estimate is used to distinguish between significant and insignificant changes in 3-D models derived from different image sets. The accuracy of the 3-D model is estimated using a general methodology, called self-consistency, for estimating the accuracy of computer vision algorithms, which does not require prior establishment of “ground truth”. A novel image-matching measure based on Minimum Description Length (MDL) theory allows us to estimate the accuracy of individual elements of the 3-D model. Experiments to demonstrate the utility of the procedure are presented