Multiview Range Image Registration Using Competitive Associative Net and Leave-One-Image-Out Cross-Validation Error

Abstract

AbstractThis paper presents a method for multiview range image registration to fuse 3D surfaces in range images taken from around an object by a laser range finder (LRF). The method uses competitive associative net (CAN2) for learning piecewise linear approximation of surfaces in the LRF range image involving various noise, and then executes pairwise registration of consecutive range images approximated by piecewise planes. To reduce the propagation error caused by the consecutive pairwise registration, the method introduces leave-one-image-out cross-validation (LOOCV) and tries to minimize the LOOCV registration error. The effectiveness is shown by using real LRF range images of several objects.KeywordsMultiview range image registrationLRF range imagesCompetitive associative netsLeave-one-image-out cross-validation