Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the primary input for sampling density reconstruction, which is effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for effective path guiding for arbitrary path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding approach can generalize to diverse testing scenes, often achieving better rendering results than previous path guiding approaches and opening up interesting future directions.

中文翻译：

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用于路径引导的光子驱动神经重建

尽管蒙特卡洛路径追踪是一种简单而有效的算法来合成照片般逼真的图像，但在涉及复杂的全局光照时，收敛到无噪声结果的速度通常很慢。最成功的方差减少技术之一是路径引导，它可以学习更好的重要性采样分布以减少像素噪声。然而，以前的方法需要大量的路径样本才能实现可靠的路径引导。我们提出了一种新颖的神经路径引导方法，该方法可以使用离线训练的神经网络从一组稀疏样本中重建高质量的路径引导采样分布。我们利用从光源追踪的光子作为采样密度重建的主要输入，这对于具有强全局照明的挑战性场景非常有效。为了充分利用我们的深度神经网络，我们将场景空间划分为自适应分层网格，在其中我们应用我们的网络为场景中的任何局部区域重建高质量的采样分布。这允许在路径跟踪中的任何位置对任意路径反弹进行有效的路径引导。我们证明了我们的光子驱动神经路径引导方法可以推广到不同的测试场景，通常比以前的路径引导方法获得更好的渲染结果，并开辟有趣的未来方向。