Gradient-domain metropolis light transport
Gradient-domain metropolis light transport
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梯度域都市轻交通
DOI：
10.1145/2461912.2461943
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Timo Aila
中科院分区：
文献类型：
--
作者：
J. Lehtinen;Tero Karras;S. Laine;M. Aittala;F. Durand;Timo Aila
我们引入了一种新颖的 Metropolis 渲染算法，该算法直接计算图像梯度，并通过求解泊松方程从梯度重建最终图像。梯度采样期间计算的图像的低保真度近似值有助于重建。作为路径空间 Metropolis 轻交通的延伸，我们的算法非常适合困难的交通场景。我们证明我们的方法在几个众所周知的测试场景中优于最先进的方法。此外，我们分析了梯度域采样的光谱特性，并将其与传统的图像域采样进行比较。
We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling.