SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Radioptimization: goal based rendering
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
The iterative solution of a nonlinear inverse problem from industry: design of reflectors
Proceedings of the international conference on Curves and surfaces in geometric design
Acquiring the reflectance field of a human face
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image-based rendering of diffuse, specular and glossy surfaces from a single image
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
A signal-processing framework for inverse rendering
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Sketching Shadows and Highlights to Position Lights
CGI '97 Proceedings of the 1997 Conference on Computer Graphics International
Inverse rendering for computer graphics
Inverse rendering for computer graphics
Lighting design: a goal based approach using optimisation
EGWR'99 Proceedings of the 10th Eurographics conference on Rendering
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This paper presents the parallelization of techniques for the design of reflector shapes from prescribed optical properties (far-field radiance distribution), geometrical constraints and, if available, a user-given initial guess. This is a problem of high importance in the field of Lighting Engineering, more specifically for Luminaire Design. Light propagation inside and outside the optical set must be computed and the resulting radiance distribution compared to the desired one in an iterative process. Constraints on the shape imposed by industry needs must be taken into account, bounding the set of possible shape definitions. A general approach is based on a minimization procedure on the space of possible reflector shapes, starting from a generic or a user-provided shape. This minimization techniques are usually known also as inverse problems, and are very expensive in computational power, requiring a long time to reach a good solution. To reduce this high resource needs we propose a parallel approach, based on SMP and clustering, that can bring the simulation times to a more feasible level.