基于时空重采样的实时随机光源裁剪

Spatiotemporal Resampling-Based Real-Time Stochastic Lightcuts

为了渲染含大量动态光源的动态场景,提出一种时空重采样算法.首先利用实时随机光源裁剪对光源进行重要性采样,生成重采样样本;然后提出一种快速时空样本有效性判断方式,通过检查几何属性与可见性,避免错误选择重采样样本而引入误差;最后通过重采样复用时空样本,依样本权重选择重要样本进行着色,提高样本质量.自建场景与NVIDIA ORCA场景的实验结果表明,所提算法能够取得较好的采样质量,通过修改空间重采样数可平衡该算法的耗时与渲染质量,使之具有一定的拓展性;与实时随机光源裁剪算法相比,在相同采样数时该算法能够取得更高的渲染质量,而渲染质量相近时该算法耗时可减少20%~40%.

This paper presented a spatiotemporal resampling algorithm for rendering dynamic scenes with many dynamic lights.Firstly,performed importance sampling by real-time stochastic lightcuts to generate resampling samples.Secondly,a fast algorithm to judge the validity of spatiotemporal samples is proposed,which avoided errors due to wrong selection of resampling samples by checking geometric properties and visibility.Finally,resampled spatiotemporal samples and selected important samples by weight to improve quality of samples.The experimental results in self-building scenes and NVIDIA ORCA indicate that this algorithm can achieve good sampling quality,and the time consuming and rendering quality of this algorithm can be balanced by modifying the spatial resampling quantity to make it scalable.Compared with real-time stochastic lightcuts algorithm,this algorithm can achieve higher rendering quality with same sample quantity and reduces 20%to 40%sampling consumption with similar rendering quality.