Fast Global Illumination Including Specular Effects Xavier Granier 1 George Drettakis 1 Bruce J. Walter 2 1 iMAGIS -GRAVIR/IMAG-INRIA iMAGIS is a joint project of CNRS/INRIA/UJF/INPG 2 Cornell University

iMAGIS EGWR /06/2000 Motivation  Realistic Illumination All light paths  Time-Quality Tradeoff  Interactive Visualisation  Quality Control

iMAGIS EGWR /06/2000 Talk overview  Previous work  New Integrated Algorithm  Results  Conclusion

iMAGIS EGWR /06/2000 Previous Work Deterministic methods  Radiosity [Goral84,Cohen88,etc]  Hierarchy and Clustering [Hanrahan91, Smits94, Sillion95, etc]  Non diffuse [ Immel86, Sillion89, Sillion91, etc] Probabilistic  Photon Map [Jensen96,etc]  Density Estimation [Walter97,etc]

iMAGIS EGWR /06/2000 Previous Work Multi-pass  Two-pass [Wallace97,Sillion89,etc]  Integrated [Chen91,etc] Interactive viewing  Render-Cache [Walter99]  Directional Storage [Stamminger99,etc]

iMAGIS EGWR /06/2000 Overview  DD transfer  Hierarchical Radiosity with Clustering (HRC)  DS + D transfer  Particle tracing during HRC gather D = Diffuse and S = Non Diffuse Images have specular path to eye added by Ray-Tracing

iMAGIS EGWR /06/2000 Algorithm Overview  Construct hierarchy  Hierarchy elements: clusters and surfaces  For each iteration  Refine  create links at correct level  Gather - Energy transfer  particle emission restricted by links  Push-pull  particle placement

iMAGIS EGWR /06/2000 Refinement  Link placement  Choose appropriate hierarchy level for transfer  Refinement test: Energy >   Visibility classification and computation  Shafts and blocker lists for classification/optimisation  Unoccluded form factor computation

iMAGIS EGWR /06/2000 I RS = Radiosity x Form Factor x Visibility Energy transfer through a link Diffuse-Diffuse transfer I RS I R = I R + I RS

iMAGIS EGWR /06/2000 Energy transfer through links Diffuse-Specular transfer  Diffuse-Specular transfer  Probabilistic emission of particles  Reflection on receiver  Propagation and impact storage  Links guide particles  Links encode light flow  Restrict number of particles

iMAGIS EGWR /06/2000 Particle Emission  Number of particles  Flux S to R / Constant energy  Uniform sampling  Inverse of ( Measure(R) x Measure(S) )  Particle power  Flux from s to r corrected by  number of particles and  probability of sample choice

iMAGIS EGWR /06/2000 Push-Pull  Push: Hierarchy descent  Particle placement  Integrate particle power into irradiance  Radiosity computation on leaves  Pull: Radiosity averaging

iMAGIS EGWR /06/2000 Particle Placement Detect high variation and concentration  Quantity  Average position and "Spread Factor"  Push particle if:  High concentration and high energy

iMAGIS EGWR /06/2000 Interactive Visualisation  Computed Solution: Diffuse part  View independant solution  Hardware rendering  Ray Trace: View dependant part  Save image  Interactivity: Render - Cache

iMAGIS EGWR /06/2000 Results: Quality control 4 sec 1200 particles 5 sec 7800 particles 15 sec particles Vary  ct parameter

iMAGIS EGWR /06/2000 Indirect 1 min 42 sec 4 min 34 sec

iMAGIS EGWR /06/2000 Particle tracing comparison Complex, indirectly lit scene simulation  10 min Particle trace Our method

iMAGIS EGWR /06/2000 Video VIDEO

iMAGIS EGWR /06/2000 Conclusion  Integrated algorithm  Hierarchical Radiosity with Clustering and Particle Tracing  Guide particle emission with Links  Place particles during push-pull  Handles indirect light well  Rapid computation  Interactive simulations for small scenes  Fast coarse solutions for complex scenes

iMAGIS EGWR /06/2000 Future Work  Separate Reconstruction  Low and High frequencies  Dynamic updates  Partial particle shooting  Distributed/Monte-Carlo Ray-trace  Solution with importance  Local precise solution  Detect needed interactions

iMAGIS EGWR /06/2000 The End