We introduce stochastic lightcuts by combining the lighting approximation of lightcuts with stochastic sampling for efficiently rendering scenes with a large number of light sources. Our stochastic lightcuts method entirely eliminates the sampling correlation of lightcuts and replaces it with noise. To minimize this noise, we present a robust hierarchical sampling strategy, combining the benefits of importance sampling, adaptive sampling, and stratified sampling. Our approach also provides temporally stable results and lifts any restrictions on the light types that can be approximated with lightcuts. We present examples of using stochastic lightcuts with path tracing and indirect illumination with virtual lights, achieving more than an order of magnitude faster render times than lightcuts by effectively approximating direct illumination using a small number of light samples, in addition to providing temporal stability. Our comparisons to other stochastic sampling techniques demonstrate that we provide superior sampling quality that matches and improves the excellent convergence rates of the lightcuts approach.

中文翻译：

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用于对许多灯光进行采样的随机 Lightcut

我们通过将光切的照明近似与随机采样相结合来引入随机光切，以有效地渲染具有大量光源的场景。我们的随机 lightcuts 方法完全消除了 lightcuts 的采样相关性，并将其替换为噪声。为了最大限度地减少这种噪音，我们提出了一种强大的分层抽样策略，结合了重要性抽样、自适应抽样和分层抽样的优点。我们的方法还提供了时间稳定的结果，并解除了对可以用光切近似的光类型的任何限制。我们展示了使用带有路径追踪的随机光刻和使用虚拟光的间接照明的示例，除了提供时间稳定性之外，通过使用少量光样本有效地近似直接照明，实现比光切快一个数量级的渲染时间。我们与其他随机采样技术的比较表明，我们提供了卓越的采样质量，匹配并提高了 lightcuts 方法的出色收敛速度。