Next View Planning for Shape from Silhouette

In order to create a complete three-dimensional model of an object based on its two-dimensional images, the images have to be acquired from different views. An increasing number of views generally improves the accuracy of the final 3D model but it also increases the time needed to build the model. The number of the possible views can theoretically be infinite. Therefore, it makes sense to try to reduce the number of views to a minimum while preserving a certain accuracy of the model, especially in applications for which the performance is an important issue. This paper shows an approach to Next View Planning for Shape from Silhouette for 3d shape reconstruction with minimal different views. Results of the algorithm developed are presented for both synthetic and real input images.

[1]  Michael Potmesil Generating octree models of 3D objects from their silhouettes in a sequence of images , 1987, Comput. Vis. Graph. Image Process..

[2]  Richard Szeliski,et al.  Rapid octree construction from image sequences , 1993 .

[3]  Olivier D. Faugeras,et al.  3D articulated models and multi-view tracking with silhouettes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[5]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[6]  A. David Marshall,et al.  Automatically planning the inspection of three-dimensional objects using stereo computer vision , 1996, Other Conferences.

[7]  Glenn H. Tarbox,et al.  Planning for Complete Sensor Coverage in Inspection , 1995, Comput. Vis. Image Underst..

[8]  Jake K. Aggarwal,et al.  Volumetric Descriptions of Objects from Multiple Views , 1983, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Peter Kovesi,et al.  Automatic Sensor Placement from Vision Task Requirements , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Konstantinos A. Tarabanis,et al.  Computing viewpoints that satisfy optical constraints , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[12]  Jake K. Aggarwal,et al.  Volume/surface octrees for the representation of three-dimensional objects , 1986, Comput. Vis. Graph. Image Process..

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

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

[15]  S. Suess,et al.  Three dimensional modelling , 1974 .

[16]  Roberto Cipolla,et al.  Structure and motion from silhouettes , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Takeo Kanade,et al.  Sensor placement design for object pose determination with three light-stripe range finders , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[18]  Richard A. Volz,et al.  Object recognition using multiple views , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[19]  Richard Pito,et al.  A Solution to the Next Best View Problem for Automated Surface Acquisition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Wolfgang Niem,et al.  Robust and fast modeling of 3D natural objects from multiple views , 1994, Electronic Imaging.

[21]  Éric Marchand,et al.  Controlled camera motions for scene reconstruction and exploration , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Glenn H. Tarbox,et al.  IVIS: An Integrated Volumetric Inspection System , 1995, Comput. Vis. Image Underst..