A Solution to the Next Best View Problem for Automated CAD Model Acquisition of Free-Form Objects Using Range Cameras

To acquire the complete description of an arbitrary object\u27s surface using a range camera, multiple images of the object from different viewpoints must be combined. This paper presents a solution to the problem of determining how to position the range camera so as to sample more of the object\u27s surface while making sure the new image can be registered and integrated into the (incomplete) model of the object. Requirements for the solution to this problem are 1) all portions of the surface which can be scanned will be scanned; 2) the solution will generate a set of views which when combined will converge to the object\u27s surface in a minimum number of images; 3) all surfaces of the object should be scanned with at least a given minimum confidence; 4) the solution will be robust in the sense that if it chooses a viewing position for the camera the resulting image will be successfully registered and integrated with the (incomplete) model or it will announce that now such view exists. We present a framework for such a solution and argue that it satisfies conditions 1 and 2. This sets the foundation to extend the present solution to satisfy conditions 3 and 4

Registration of free-form surfaces represented by point sets

A method has been developed for registering two dense 3-D maps obtained by using a correlation-based stereo system . Geometric matching in general is a difficult unsolved problem in computer vision . Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually either small or approximately known . From this initial estimate, our algorithm can compute the motion with very good precision, which is required for environment modeling . Objects are represented by a set of 3-D points, which are considered as the samples of a surface . No contraint is imposed on the foret of the objects . The proposed algorithm is based on iteratively matching points of one view to the closest points of the another view . A statistical method based on the distance distribution is used ta discard the outliers. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Real data have been used to test the algorithm . The results show that it is efficient and robust, and yields an accurate motion estimate.Une méthode a été développée pour recaler deux nuages de points 3D obtenus en utilisant la stéréo par corrélation. Le recalage de deux ensembles de primitives géométriques est un problème en général très difficile et non résolu. Heureusement, dans beaucoup d'applications, des connaissances a priori simplifient considérablement le problème. Par exemple, le mouvement entre deux positions successives est généralement soit petit soit approximativement connu. A partir de cette estimée grossière, notre algorithme permet de calculer le mouvement avec une très bonne précision, nécessaire à l'obtention d'un modèle satisfaisant de l'environnement. Les objets observés sont représentés au moyen de nuages de points 3D. Ces points sont considérés comme des échantillons d'une surfac