Radioptimization: goal based rendering

This paper presents a method for designing the illumination in an environment using opti mization techniques applied to a radiosity based image synthesis system An optimization of lighting parameters is performed based on user speci ed constraints and objectives for the illumination of the environment The system solves for the best possible settings for light source emissivities element re ectivities and spot light directionality parameters so that the design goals such as to minimize energy or to give the the room an impression of privacy are met The system absorbs much of the burden for searching the design space allowing the user to focus on the goals of the illumination design rather than the intricate details of a complete lighting speci cation A software implementation is described and some results of using the system are reported The system employs an object space perceptual model based on work by Tumblin and Rushmeier to account for psychophysical e ects such as subjective brightness and the visual adaptation level of a viewer This provides a higher delity when comparing the illumination in a computer simulated environment against what would be viewed in the real world Optimization criteria are based on subjective impressions of illumination with qualities such as pleasantness and privateness The qualities were selected based on Flynn s work in illuminating engineering These criteria were applied to the radiosity context through an experiment conducted with subjects viewing rendered images and the respondents evaluated with a Multi Dimensional Scaling analysis Radioptimization Goal Based Rendering John K Kawai James S Painter Department of Computer Science University of Utah Michael F Cohen Department of Computer Science Princeton University

[1]  J. B. Rosen The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints , 1960 .

[2]  J. B. Rosen The gradient projection method for nonlinear programming: Part II , 1961 .

[3]  S. S. Stevens,et al.  Brightness function: effects of adaptation. , 1963, Journal of the Optical Society of America.

[4]  R. Faure,et al.  Introduction to operations research , 1968 .

[5]  David Mautner Himmelblau,et al.  Applied Nonlinear Programming , 1972 .

[6]  J. E. Flynn,et al.  Interim Study of Procedures for Investigating the Effect of Light on Impression and Behavior , 1973 .

[7]  Dimitri P. Bertsekas,et al.  Numerical methods for constrained optimization , 1976 .

[8]  J. E. Flynn A study of subjective responses to low energy and non-uniform lighting systems , 1977 .

[9]  John E. Flynn,et al.  A Guide to Methodology Procedures for Measuring Subjective Impressions in Lighting , 1979 .

[10]  Donald P. Greenberg,et al.  The hemi-cube: a radiosity solution for complex environments , 1985, SIGGRAPH.

[11]  James T. Kajiya,et al.  The rendering equation , 1986, SIGGRAPH.

[12]  Donald P. Greenberg,et al.  An Efficient Radiosity Approach for Realistic Image Synthesis , 1986, IEEE Computer Graphics and Applications.

[13]  Prafulla C. Sorcar,et al.  Architectural Lighting for Commercial Interiors , 1987 .

[14]  R. Fletcher Practical Methods of Optimization , 1988 .

[15]  Donald P. Greenberg,et al.  A progressive refinement approach to fast radiosity image generation , 1988, SIGGRAPH.

[16]  Paul E. Green,et al.  Multidimensional Scaling: Concepts and Applications , 1989 .

[17]  William H. Press,et al.  Numerical recipes , 1990 .

[18]  Jack Tumblin,et al.  Tone Reproduction for Realistic Computer Generated Images , 1991 .

[19]  Pat Hanrahan,et al.  A rapid hierarchical radiosity algorithm , 1991, SIGGRAPH.

[20]  HanrahanPat,et al.  A rapid hierarchical radiosity algorithm , 1991 .

[21]  Pierre Poulin,et al.  Lights from highlights and shadows , 1992, I3D '92.

[22]  Carlo H. Séquin,et al.  Management of large amounts of data in interactive building walkthroughs , 1992, I3D '92.