A sensor-based solution to the "next best view" problem

Acquiring the complete surface geometry of an object using a range scanner invariably requires that multiple range images be taken of it from different viewpoints. An algorithm is presented which solves the "next best view" (NBV) problem: determine the next position for the range scanner given its previous scans of the object. As part of a complete surface acquisition system the scanner's next position should cause it not only to sample more of the object's surface but to resample part of the object already scanned to allow for the registration and integration of the new data with the previous scans. A novel representation, positional space, is presented which facilitates a solution to the NBV problem by representing what must be and what can be scanned in a unified data structure. The expensive operation of determining the visibility of part of the viewing volume is computed only once, not for each potential position of the scanner thus breaking the computational burden of choosing the NBV from a large number of positions. No assumptions are made about the geometry or topology of the object. The algorithm is self-terminating will scan all visible surfaces of an object and can be directed to resample surfaces which were scanned with low confidence. In addition, the algorithm will work with nearly any range camera and scanning setup. A completely automated surface acquisition system featuring the proposed NBV algorithm is demonstrated on a real object.

[1]  Frank P. Ferrie,et al.  Autonomous exploration: driven by uncertainty , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Richard Pito,et al.  Mesh integration based on co-measurements , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[3]  C. Ian Connolly,et al.  The determination of next best views , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[4]  Konstantinos A. Tarabanis,et al.  A survey of sensor planning in computer vision , 1995, IEEE Trans. Robotics Autom..

[5]  Ruzena Bajcsy,et al.  Occlusions as a Guide for Planning the Next View , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.

[7]  Adrian Hilton,et al.  Reliable Surface Reconstructiuon from Multiple Range Images , 1996, ECCV.

[8]  William E. Lorensen,et al.  Decimation of triangle meshes , 1992, SIGGRAPH.

[9]  Marc Levoy,et al.  Zippered polygon meshes from range images , 1994, SIGGRAPH.

[10]  Lambert E. Wixson,et al.  Viewpoint selection for visual search , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..