Antoine Scardigli

RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing

Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time applications. To address this, we propose a framework with end-to-end training of a sampling importance network, a latent space enc…