How to Train Your Renderer: Optimized Methods for Learning Path Distributions in Monte Carlo Light Transport
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2025-05-06
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Abstract
Light transport simulation allows us to preview architectural marvels before they break ground, practice complex surgeries without a living subject, and explore alien worlds from the comfort of our homes. Fueled by the steady advancements in computer hardware, rendering virtual scenes is more accessible than ever, and is met by an unprecedented demand for such content. Light interacts with our world in various intricate ways, hence the challenge in realistic rendering lies in tracing all the possible paths that light could take within a given virtual scene. Contemporary approaches predominantly rely on Monte Carlo integration, for which countless sampling procedures have been proposed to handle certain families of effects robustly. Handling all effects holistically through specialized sampling routines, however, remains an unsolved problem.
A promising alternative is to use learning techniques that automatically adapt to the effects present in the scene. However, such approaches require many complex design choices to be made, which existing works commonly resort to heuristics for. In this work, we investigate what constitutes effective learning algorithms for rendering – from data representation and the quantities to be learned, to the fitting process itself. By strategically optimizing these components for desirable goals, such as overall render efficiency, we demonstrate significant improvements over existing approaches.
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