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Active Exploration for Neural Global Illumination of Variable Scenes
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DescriptionWe introduce a novel active exploration method using Markov Chain Monte Carlo, which explores the space D of possible training data instances, generating ground truth renderings that best help training. After training, our method allows interactive rendering of hard light transport paths (e.g., complex caustics) and scene manipulation.