Eficiência e precisão no cálculo de iluminação por radiosidade com GPUs
AUTOR(ES)
Alessandro Ribeiro da Silva
DATA DE PUBLICAÇÃO
2008
RESUMO
This work consists of a study about the global illumination model known as radiosity, in an effort to achieve an efficient implementation on modern hardware. Radiosity is a classic and very important method in computer graphics. It is the first proposed method to achieve photo-realism as it considers interactions among the objects of the scene. As the method has a high processing demand, we propose the use of modern GPUs, with their massive processing capacity, in an attempt to speed up the computation. We experimented with two distinct implementations that use GPUs to accelerate part or all of the method. The first implementaion is similar to the original proposal of radiosity, and the GPUs were used to solve the simultaneous linear equations. The second implementation is derived from an extension to the original radiosity method that was designed for processing in GPUs. As results, we compared different methods to solve the simultaneous linear equations, on both GPUs and CPUs and we proposed an improvement on the GPU implementation related to the determination of visibility. Our modifications improves the quality of the output image while preserving the interactive charateristic of the method variation.
ASSUNTO(S)
processamento de imagens técnicas digitais teses. processamento paralelo (computadores) teses. computação teses.
ACESSO AO ARTIGO
http://hdl.handle.net/1843/RVMR-7PVNGKDocumentos Relacionados
- Calculo distribuido do fator de forma do método radiosidade em ambiente fracamente acoplado
- Eficiência energética: sistemas de iluminação com LEDs, distribuídos em corrente contínua e utilizando energia fotovoltaica
- Um algoritmo de segmentação por crescimento de regiões para GPUs
- Relação entre dimensões de janela e piso para iluminação natural e eficiência energética em edificações no trópico úmido
- Calculo da iluminação em sintese de imagens atraves de ray-tracing estocastico