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A Hierarchical Architecture for Neural Materials
Neural reflectance models are capable of reproducing the spatially-varying appearance of many real-world materials at different scales.
Photon-Driven Neural Path Guiding
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.
Deep Photon Mapping
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.
