Fast Adaptive Selection of Best Views
Automatic computation of best views of objects is very useful. For example, they can be used as the starting point of a scene exploration, or to enrich galleries of objects available through Internet by adding an image a model that may help to decide if it is worth downloading. To select the most interesting viewpoint of an object, we use the so-called viewpoint entropy. The best view is the one which gives the most information of the object being inspected. In this paper we present an adaptive method to compute best views. Our adaptive scheme allows to improve over previous approaches the time of the selection of best views by an order of magnitude, and achieve a nearly interactive rate.
Unable to display preview. Download preview PDF.
- C. Colin. Automatic computation of a scene’s good views. In Proc. MICAD, February 1990.Google Scholar
- P. Barral, G. Dorme, and D. Plemenos. Scene understanding techniques using a virtual camera. In A. de Sousa and J.C. Torres, editors, Proc. Eurographics’00, short presentations, 2000.Google Scholar
- D. Plemenos and M. Benayada. Intelligent display in scene modeling. new technique to automatically compute good views. In Proc. International Conference GRAPHICON’96, July 1996.Google Scholar
- G. Dorme. Study and implementation of 3D scenes comprehension techniques. PhD thesis, Université de Limoges, 2001. In French.Google Scholar
- E. Marchand and N. Courty. Image-based virtual camera motion strategies. In P. Poulin S. Fels, editor, Proc.of the Graphics Interface Conference, GI2000, pages 69–76, Montreal, Quebec, May 2000. Morgan Kaufmann.Google Scholar
- E. Bourque and G. Dudek. Automatic creation of image-based virtual reality. In Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, volume 3209, pages 292–303, Bellingham, WA, October 1997. SPIE-The International Society for Optical Engineering. ISBN 0819426415.Google Scholar
- L. Wong, C. Dumont, and M. Abidi. Next best view system in a 3-d object modeling task. In Proc. International Symposium on Computational Intelligence in Robotics and Automation (CIRA), pages 306–311, 1999.Google Scholar
- W. T. Freeman. Exploiting the generic view assumption to estimate scene parameters. In Proc. 4th International Conference on Computer Vision, pages 347–356, Berlin, Germany, 1993. IEEE.Google Scholar
- A. L. Yuille, J. M. Coughlan, and S. Konishi. The generic viewpoint constraint resolves the generalized bas relief ambiguity. In Proc. of Conference on Information Scienes and Systems (CISS 2000), Princeton University, March 15–17 2000.Google Scholar
- J. Marks, B. Andalman, P. A. Beardsley, W. Freeman, S. Gibson, J. Hodgins, T. Kang, B. Mirtich, H. Pfister, W. Ruml, K. Ryall, J. Seims, and S. Shieber. Design galleries: A general approach to setting parameters for computer graphics and animation. In T. Whitted, editor, SIGGRAPH 97 Conference Proceedings, Annual Conference Series, pages 389–400. ACM SIGGRAPH, Addison Wesley, August 1997. ISBN 0-89791-896-7.Google Scholar
- P.-P. Vázquez, M. Feixas, M. Sbert, and W. Heidrich. Viewpoint selection using viewpoint entropy. In T. Ertl, B. Girod, G. Greiner H. Niemann, and H.-P. Seidel, editors, Proceedings of the Vision Modeling and Visualization Conference (VMV-01), pages 273–280, Stuttgart, November 21–23 2001. IOS Press, Amsterdam.Google Scholar
- T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, 1991.Google Scholar
- S. Gumhold. Maximum entropy light source placement. In Proc. of the Visualization 2002 Conference, pages 275–282. IEEE Computer Society Press, October 2002.Google Scholar