Presentation

<
Streaming-Aware Neural Monte Carlo Rendering Framework with Unified Denoising-Compression and Client Collaboration
SessionMore Than it Looks Like
DescriptionTo reduce the high rendering costs and transmission bandwidth requirements of path tracing-based cloud rendering, we propose a novel streaming-aware rendering framework that is able to learn a joint optimal model integrating two path-tracing acceleration (adaptive sampling and denoising) and video compression technique with client side G-buffer collaboration.
Authors
Hangming Fan
State Key Laboratory of CAD & CG, Zhejiang University
Yuchi Huo
State Key Laboratory of CAD & CG, Zhejiang University
Chuankun Zheng
State Key Laboratory of CAD & CG, Zhejiang University
Chonghao Hu
State Key Laboratory of CAD & CG, Zhejiang University
Yazhen Yuan
Game Engine Department, CROS, Tencent, China
Rui Wang
State Key Laboratory of CAD & CG, Zhejiang University
Event Type
Technical Paper
TimeThursday, 14 August 20254:25pm - 4:35pm PDT
LocationWest Building, Rooms 220-222
ACM Digital Library Journal Papers' PDFs
Conference Papers' PDFs
Session Time & Location
Sunday, 10 August 20256:00pm - 8:45pm PDTWest Building, Ballroom AB
Thursday, 14 August 20253:45pm - 5:15pm PDTWest Building, Rooms 220-222
Recordings
Livestreamed
Recorded
Next PresentationNext PresentationFeeling Blue or Seeing Red? Investigating the effect of light color, shadow and realism on the perception of emotion of real and virtual humans
Similar Presentations
QUASAR: Quad-based Adaptive Streaming And Rendering
Histogram Stratification for Spatio-Temporal Reservoir Sampling
Image-GS: Content-Adaptive Image Representation via 2D Gaussians