Next-best-view algorithm for object reconstruction

Laurana M. Wong; Christophe Dumont; Mongi A. Abidi

doi:10.1117/12.327001

9 October 1998 Next-best-view algorithm for object reconstruction

Laurana M. Wong, Christophe Dumont, Mongi A. Abidi

Proceedings Volume 3523, Sensor Fusion and Decentralized Control in Robotic Systems; (1998) https://doi.org/10.1117/12.327001
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

The goal of sensor planning in 3D object reconstruction is to acquire enough 2D information about an object to create a 3D model of that object. This requires the acquisition of images from different viewpoints, the registration of each view, and the integration of all acquired information into a single model. Our focus is on the acquisition stage, we want to determine sensor poses or viewpoints that provide the next best view in an object reconstruction task. The Next Best View (NBV) is the determination of a new sensor position that reveals an optimal amount of unknown information about the object being modeled. The goal of a NBV reconstruction system is usually to model an object using the smallest number of views. Our approach to complete this task is to study a volumetric representation of the model after a new view is obtained. Since the volumetric model consists of known and unknown information, we want to find the viewpoint from which the largest amount of unknown data can be acquired. The NBV algorithm is integrated into a system that acquires synthetic range data from a computer object model, calculates new viewpoints according to an objective function, and reconstructs a complete volumetric model of the object of interest. The NBV algorithm is described in depth and experimental results are given. Also included is a review of related work.

Citation Download Citation

Laurana M. Wong, Christophe Dumont, and Mongi A. Abidi "Next-best-view algorithm for object reconstruction", Proc. SPIE 3523, Sensor Fusion and Decentralized Control in Robotic Systems, (9 October 1998); https://doi.org/10.1117/12.327001

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