并行多维自适应采样

针对现有自适应采样方法绘制效果差和速度慢的问题,提出一种并行的多维自适应采样方法.首先对多维空间进行粗采样,将其自适应地分割为多个子空间；然后扩展各子空间边界,根据噪声评价值分配每个子空间所需的采样点数；在各子空间上构建KD树以并行地对其进行自适应采样；最后根据各采样点间的梯度值重构图像.实验结果表明,该方法能够以更少的样本绘制高质量的景深、运动模糊和软阴影效果,并控制绘制图像时的内存消耗,支持高分辨率的真实感图像生成.

Parallel Multidimensional Adaptive Sampling

To address the problems of current adaptive sampling approaches,we present a parallel multidimensional adaptive sampling method.Firstly,the multidimensional space is coarsely sampled and adaptively split into subspaces.Secondly,the borders of each subspace are extended and the number of samples is assigned to each subspace according to its size.Thirdly,a KD-tree is adaptively constructed in parallel for subspace.Finally,the subspaces are reconstructed and combined into an entire image by leveraging the gradient values of sampling points.The experimental results demonstrated that our algorithm takes less memory,runs faster than existing multidimensional adaptive sampling methods,and can generate images rapidly with satisfying effects of motion blur,depth-of-field and soft shadows.