Recently, deep learning‐based denoising approaches have led to dramatic improvements in low sample‐count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high‐quality reconstructions. In this paper, we develop the first deep learning‐based method for particle‐based rendering, and specifically focus on photon density estimation, the core of all particle‐based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per‐photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per‐photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high‐quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods. Our approach largely reduces the required number of photons, significantly advancing the computational efficiency in photon mapping.

中文翻译：

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光子映射的深度核密度估计

最近，基于深度学习的去噪方法在低样本数蒙特卡罗渲染方面取得了显着进步。这些方法针对的是路径追踪，这对于模拟具有挑战性的光传输效果（如焦散）而言并不理想，其中光子映射是首选方法。然而，光子映射需要非常大量的跟踪光子才能实现高质量的重建。在本文中，我们开发了第一个基于深度学习的基于粒子的渲染方法，特别关注光子密度估计，这是所有基于粒子的方法的核心。我们训练了一个新的深度神经网络来预测核函数以在着色点聚合光子贡献。我们的网络将单个光子编码为每个光子的特征，在着色点附近聚合它们以构建光子局部上下文向量，并从每个光子和光子局部上下文特征推断核函数。该网络很容易结合到许多以前的光子映射方法中（通过简单地交换核密度估计器），并且可以生成复杂全局照明效果的高质量重建，例如焦散，与以前的光子映射方法相比，光子数量减少了一个数量级。我们的方法在很大程度上减少了所需的光子数量，显着提高了光子映射的计算效率。该网络很容易结合到许多以前的光子映射方法中（通过简单地交换核密度估计器），并且可以生成复杂全局照明效果的高质量重建，例如焦散，与以前的光子映射方法相比，光子数量减少了一个数量级。我们的方法在很大程度上减少了所需的光子数量，显着提高了光子映射的计算效率。该网络很容易结合到许多以前的光子映射方法中（通过简单地交换核密度估计器），并且可以生成复杂全局照明效果的高质量重建，例如焦散，与以前的光子映射方法相比，光子数量减少了一个数量级。我们的方法在很大程度上减少了所需的光子数量，显着提高了光子映射的计算效率。