Abstract
The automatic selection of good viewing parameters is very complex. In most cases, the notion of good strongly depends on the concrete application. Moreover, when an intuitive definition of good view is available, it is often difficult to establish a measure that brings it to the practice. Commonly, two kind of viewing parameters must be set: the position and orientation of the camera, and the ones relative to light sources. The first ones will determine how much of the geometry can be captured and the latter will influence on how much of it is revealed (i. e. illuminated) to the user. In this paper we will define a metric to calculate the amount of information relative to an object that is communicated to the user given a fixed camera position. This measure is based on an information-based concept, the Shannon entropy, and will be applied to the problem of automatic selection of light positions in order to adequately illuminate an object.
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References
C. Colin. Automatic computation of a scene’s good views. In Proc. MICAD, February 1990.
T. Kamada and S. Kawai. A simple method for computing general position in displaying three-dimensional objects. Computer Vision, Graphics, and Image Processing, 41(1):43–56, January 1988.
D. Plemenos and M. Benayada. Intelligent display in scene modeling. new techniques to automatically compute good views. In Proc. International Conference GRAPHICON’96, July 1996.
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.
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.
R. E. Blahut. Principles and Practice of Information Theory. Addison-Wesley, Cambridge, MA, 1987.
P.-P. Vázquez, M. Feixas, M. Sbert, and A. Llobet. Viewpoint entropy: A new tool for obtaining good views for molecules. In D. Ebert, P. Brunet, and I. Navazo, editors, Data Visualisation 2002 (Eurographics/IEEE TCVG Symposium Proceedings). Eurographics/IEEE, May 27–29 2002.
Y. Takeuchi and N. Ohnishi. Active vision system based on information theory. Systems and Computers in Japan, 29 (11):31–39, 1998.
G. Patow and X. Pueyo. A survey on inverse rendering problems. Technical Report IIiA 00-07-RR, IIiA, University of Girona, 2000.
C. Schoeneman, J. Dorsey, B. Smits, J. Arvo, and D. Greenberg. Painting with light. In James T. Kajiya, editor, SIGGRAPH 93 Conference Proceedings, Computer Graphics Proceedings, Annual Conference Series, pages 143–146. ACM SIGGRAPH, ACM Press, August 1993.
J. K. Kawai, J. S. Painter, and M. F. Cohen. Radioptimization-goal based rendering. In Proc. of SIGGRAPH-93: Computer Graphics, pages 147–154, Anaheim, CA, 1993.
A. Cardoso Costa, A. Augusto de Sousa, and F. Nunus Ferreira. Lighting design: A goal based approach using optimization. In Rendering Techniques’ 99, pages 317–328, New York, NY, 1999. Springer Wien.
P. Poulin and A. Fournier. Lights from highlights and shadows. Computer Graphics, 25(2):31–38, March 1992.
P. Poulin, K. Ratib, and M. Jacques. Sketching shadows and highlights to position lights. In Proceedings of Computer Graphics International 97, pages 56–63. IEEE Computer Society, June 1997.
V. Jolivet, D. Plemenos, and P. Poulingeas. Inverse direct lighting with a Monte Carlo method and declarative modelling. 2330:3–--, 2002.
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.
S. Gumhold. Maximum entropy light source placement. In Proc. of the Visualization 2002 Conference, pages 275–282. IEEE Computer Society Press, October 2002.
R. Shacked and D. Lischinski. Automatic lighting design using a perceptual quality metric. In Computer Graphics Forum (Proceedings of Eurographics 2001), volume 20, pages C-215–C-226, September 2001.
L. Neumann, K. Matkovic, and W. Purgathofer. Perception based color image difference. Computer Graphics Forum, 17(3):233–241, September 1998.
J. Kautz, P.-P. Vázquez, W. Heidrich, and H.-P. Seidel. A Unified Approach to Prefiltered Environment Maps. In B. Peroche and H. Rushmeier, editors, Eleventh Eurographics Rendering Workshop 2000, pages 185–196. Eurographics, June 2000.
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Vázquez, PP., Sbert, M. (2003). Perception-Based Illumination Information Measurement and Light Source Placement. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44842-X_32
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DOI: https://doi.org/10.1007/3-540-44842-X_32
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