Adaptive Rendering Based on Weighted Local Regression
Adaptive Rendering Based on Weighted Local Regression
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DOI：
10.1145/2641762
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Bochang Moon;N. Carr;Sung-eui Yoon
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文献类型：
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作者：
Bochang Moon;N. Carr;Sung-eui Yoon
蒙特卡罗光线跟踪被认为是渲染照片级真实感图像的最有效的技术之一，但需要大量的光线样本来生成收敛的甚至视觉上令人愉悦的图像。提出了一种基于局部回归理论的图像平面自适应采样与重建方法。提出了一种新的局部空间估计过程，采用局部回归，通过鲁棒地解决噪声高维特征。鉴于对估计局部空间的局部回归，我们提供了一种新颖的两步优化过程，用于以数据驱动的方式局部选择特征带宽。然后使用计算的带宽应用局部加权回归，以产生具有良好保留的细节的平滑图像重建。我们推导出一个误差分析，以指导我们的自适应采样过程中的局部空间。我们证明，我们的方法产生更准确和视觉上令人愉快的结果在国家的最先进的技术在广泛的渲染效果。我们的方法还允许用户使用任意一组特征，包括噪声特征，并通过忽略噪声特征并将其解相关以获得更高的质量来稳健地计算其中的一个子集。
Monte Carlo ray tracing is considered one of the most effective techniques for rendering photo-realistic imagery, but requires a large number of ray samples to produce converged or even visually pleasing images. We develop a novel image-plane adaptive sampling and reconstruction method based on local regression theory. A novel local space estimation process is proposed for employing the local regression, by robustly addressing noisy high-dimensional features. Given the local regression on estimated local space, we provide a novel two-step optimization process for selecting bandwidths of features locally in a data-driven way. Local weighted regression is then applied using the computed bandwidths to produce a smooth image reconstruction with well-preserved details. We derive an error analysis to guide our adaptive sampling process at the local space. We demonstrate that our method produces more accurate and visually pleasing results over the state-of-the-art techniques across a wide range of rendering effects. Our method also allows users to employ an arbitrary set of features, including noisy features, and robustly computes a subset of them by ignoring noisy features and decorrelating them for higher quality.