论文标题
蒙特卡洛渲染的感知错误优化
Perceptual error optimization for Monte Carlo rendering
论文作者
论文摘要
综合逼真的图像涉及计算高维光传输积分。在实践中，这些积分是通过蒙特卡洛整合估算的。该估计的误差表现为显着的混叠或噪声。为了改善此类工件并改善图像保真度，我们提出了一个面向感知的框架，以优化蒙特卡洛渲染的误差。我们利用基于Halftoning文献的人类感知的模型。结果是一个优化问题，其解决方案将误差分布在图像空间中的视觉令人愉悦的蓝色噪声。为了找到解决方案，我们提出了一系列算法，这些算法在质量和速度之间提供了不同的权衡，对先前的最新状态显示了实质性的改进。我们使用定量和误差指标进行评估，并提供广泛的补充材料，以证明我们方法所获得的感知改进。
Synthesizing realistic images involves computing high-dimensional light-transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics, and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods.
