We introduce an efficient Two-Level Monte Carlo (subset of Multi-Level Monte Carlo, MLMC) estimator for real-time rendering of scenes with global illumination. Using MLMC we split the shading integral into two parts: the radiance cache integral and the residual error integral that compensates for the bias of the first one. For the first part, we developed the Neural Incident Radiance Cache (NIRC) leveraging the power of fully-fused tiny neural networks as a building block, which is trained on the fly. The cache is designed to provide a fast and reasonable approximation of the incident radiance: an evaluation takes 2-25x less compute time than a path tracing sample. This enables us to estimate the radiance cache integral with a high number of samples and by this achieve faster convergence. For the residual error integral, we compute the difference between the NIRC predictions and the unbiased path tracing simulation. Our method makes no assumptions about the geometry, materials, or lighting of a scene and has only few intuitive hyper-parameters. We provide a comprehensive comparative analysis in different experimental scenarios. Since the algorithm is trained in an on-line fashion, it demonstrates significant noise level reduction even for dynamic scenes and can easily be combined with other importance sampling schemes and noise reduction techniques.

翻译：我们提出了一种高效的两级蒙特卡洛（多级蒙特卡洛MLMC的子集）估计器，用于实现全局光照场景的实时渲染。利用MLMC，我们将着色积分分解为两部分：辐射缓存积分和用于补偿前者偏差的残差误差积分。对于第一部分，我们开发了神经入射辐射缓存（NIRC），利用完全融合的微型神经网络作为构建模块，并在线训练。该缓存旨在提供快速且合理的入射辐射近似：其单次评估所需的计算时间比路径追踪采样少2-25倍。这使得我们能够用大量样本来估计辐射缓存积分，从而实现更快的收敛。对于残差误差积分，我们计算NIRC预测与无偏路径追踪模拟之间的差值。我们的方法不对场景的几何结构、材质或光照做任何假设，且仅包含少数直观的超参数。我们在不同实验场景中提供了全面的对比分析。由于该算法以在线方式训练，即使在动态场景下也能显著降低噪声水平，并且可以轻松与其他重要性采样方案及降噪技术结合使用。