【摘要】 基于物理的光线追踪算法用于从三维场景模型生成逼真的二维图像,光线追踪渲染较为耗时,所以如何提高算法的效率成为研究热点。针对斯坦福大学经典的多线程光线追踪引擎——PBRT,考虑任务划分粒度和负载均衡等因素,基于两级任务划分体系,提出了动态自适应分布式并行光线追踪算法。实验中在保证高质量图像生成的前提下,使用80个CPU核时,改进算法比PBRT原算法获得了近乎线性的加速比。实验结果表明改进算法具有良好的效率和扩展性,能够有效地用于光线追踪成像,提高光线追踪成像效率。更多还原

【Abstract】 Ray tracing is a technique for image synthesis: creating a photorealistic picture of a 3-D world. But the technique tends to be a time-consuming process, and how to decrease the expensive computational cost has become a hotspot. Considering the factors of task partition and load balance, with the two levels of task partition method, a new dynamically selfadaptive distributed ray tracing algorithm was developed to enhance the efficiency of ray tracing based on Physically Based Ray Tracing( PBRT), which is a famous ray tracing framework from Stanford University. When using CPU cores to 80 in experiment, the corresponding speedup of the proposed algorithm versus the original PRBT algorithm reached to a perfect value, which is close to theoretic linear speedup. It shows that the proposed algorithm is effective and highly scalable with respect to PBRT. The proposed method can be applied to ray tracing process effectively to accelerate the efficiency of rendering.更多还原