Importance-Based Ray Strategies for Dynamic Diffuse Global Illumination

I3D 2023
Huawei Technologies, Canada

IS-DDGI achieves significant speedups over DDGI while maintaining similar visual quality.


In this paper, we propose a first and efficient ray allocation technique for Dynamic Diffuse Global Illumination (DDGI) using Multiple Importance Sampling (MIS). Our technique, IS-DDGI, extends DDGI by incorporating a set of importance-based ray strategies that analyze, allocate, and manage ray resources on the GPU. We combine these strategies with an adaptive historical and temporal frame-to-frame analysis for an effective reuse of information and a set of GPU-based optimizations for speeding up ray allocation and reducing memory bandwidth. Our IS-DDGI achieves similar visual quality to DDGI with a speedup of 1.27x to 2.47x in total DDGI time and 3.29x to 6.64x in probes ray tracing time over previous technique. Most speedup of IS-DDGI comes from probes ray tracing speedup.


  author    = {Liu, Zihao and Huang, Jing and Rocha, Allan and Malmros, Jim and Zhang, Jerry},
  title     = {Importance-Based Ray Strategies for Dynamic Diffuse Global Illumination},
  journal   = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
  volume    = {6},
  number    = {1},
  month     = {May},
  year      = {2023}