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2017 GTC San Jose

S7256 - Learning Light Transport the Reinforced Way

Session Speakers
Session Description

We show that the equations for reinforcement learning and light transport simulation are related integral equations. After a brief introduction of reinforcement learning and light transport simulation we visualize the correspondence between the equations by pattern matching. Based on this correspondence, a scheme to learn importance during sampling path space is derived. The new approach is demonstrated in a consistent light transport simulation algorithm that uses reinforcement learning to progressively learn probability density functions for importance sampling. Furthermore we show that our method is easy to integrate into any existing path tracer and can greatly increase rendering efficiency.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Rendering and Ray Tracing
Architecture / Engineering / Construction Government / National Labs Higher Education / Research Manufacturing Media & Entertainment Software
25 minutes
Session Schedule