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Progressive photon mapping
- 1. Progressive Photon Mapping T. Hachisuka, S. Ogaki, H.W. Jensen
- 2. Monte Carlo Photon Mapping Progressive Photon Mapping Results
- 3. Monte Carlo Techniques Path Tracing Light Tracing Bidirectional Path Tracing Metropolis Light Transport
- 4. Caustics
- 10. Lights in Glass
- 11. Water
- 12. Swimming Pool
- 13. Monte Carlo Photon Mapping Progressive Photon Mapping Results
- 14. SDS Paths
- 15. Photon Mapping
- 18. Photon Density Estimate ππ₯=πππ2 Β
- 19. Weakness
- 20. Quality is Memory Limited
- 21. Monte Carlo Photon Mapping Progressive Photon Mapping Results
- 22. Photon Mapping Photon Tracing Ray Tracing
- 23. Reverse Photon Shooting Ray Tracing Photon Tracing
- 26. Progressive Photon Mapping Ray Tracing Photon Tracing
- 29. Multipass Density Estimate πΒ ->0 Β
- 30. Radius Reduction Assume homogeneous density π=π+πππ Β π=ππ 2π Β
- 31. Radius Reduction Consistency requirement: π>πβπ=π+πΌπ New Radius: βπ =Rπ+πΌπΒ π+π Β
- 32. Flux Correction ππΒ β Β ππ + ππ Assume homogeneous illumination ππ=ππΒ +Β ππππ ππ =ππΒ +Β πππ+πΌππ+π Β
- 33. Overview Raytracing first Multiple photon tracing passes Radius reduction Flux correction
- 34. Monte Carlo Photon Mapping Progressive Photon Mapping Results
- 35. Results
- 36. PT BDPT Progressive Photon Mapping
Editor's Notes
- Introduce yourselfProgressive Photon MappingPaper by Hachisuka etal
- Related Techniques β Monte Carlo + Photon Mapping + ProblemsProgressive Photon MappingResults
- All share a similar problem
- Inefficient for CausticsEspecially classic path tracingRecap: What are caustics?
- ViewerLightsourceBlue -> DiffuseGreen -> Specular
- SD PathsFrom Light To SpecularTo DiffuseTo CameraProblem: Tracing from ViewerSample SemisphereRed path very improbable
- Easy way to solve problemStart tracing at lightDiffuse surface -> Force ray to camera -> no samplingOther caustics related path problematic
- Instead of Camera -> DiffuseCamera -> Specular -> Diffuse
- Cannot force ray to cameraRed path highly improbableSlowly convergingReal life examples?
- First specular bounce: Light in glassSpecular reflections in scene very inefficient
- Water in a swimming poolCaustic effects on bottom of poolSchematic
- Light enters -> specularCaustics on ground -> diffuseSeen through surface -> specularSame setup as before
- More efficient solution: Photon MappingExplain PM for special case:SDS paths
- Same setupSDS PathSimilar as Light TracingStart at lightsource
- Trace photons into sceneBounce at specular surfaceHit diffuse surface
- At diffuse surface: STORE3D structure like KD-TreeTrace millions of photonsAccess them in following pass
- RaytracingShoot rays into sceneBounce on specular surfaceOn diffuse surface: Estimate Photon Density
- Top view Middle: hit point of ray tracingFind n-Nearest photons (KD-Tree handy)Radius of disk = Distance to n-th nearest photonDensity = photons / areaDensity -> Estimate Illumination-> Good approximationBut Problem
- Quality <- # photonsStore all photons-> #photons limited by memory
- PM does not allow arbitrary precision MCPT: Calculate longer -> better qualityPM: Hardware limits quality
- PPM makes a few changes to PM
- Photon Tracing pass-> Store all photonsRay Tracing pass-> Estimate IlluminationFirst Change:
- Turn around.First Ray Tracing, then Photon Tracing.How does this work?
- Same algorithm as ray tracing in non-progressiveStore all hit points (KD-Tree)-> Structure of all visible points
- Then shoot photons. Store all photonsThen estimate illumination of each hitpointAdvantage for Progressive Photon Mapping
- We can do multiple passes of photon tracingWith just one ray tracing pass.Progressively enhance qualityWithout storing photonsNot trival
- 2 passes of photon tracingEstimate photon density of accumulation Second pass will yield similar radius
- When merging both casesK-Nearestneighbours should return smaller radius
- Represent growing frequencyRadius converge to zeroProblem: N-Nearest neighbors not possibleWe need an estimate
- Assume homogeneousDetermine densityMultiply with new area to get number of photonsDetermine R
- For consistencyConverge to right solutionAdd photons in every iteration-> new parameter alpha-> fraction of photons to keepKnow number of photons -> determine radius
- Another problem:Accumulate flux every iterationFlux depends on area. Cannot simply add flux, correct by ratio of areas
- More efficient solution: Photon MappingExplain PM for special case:SDS paths
- PT badLT good on diffuse surfaceBad on specular -> cameraPM good, but low freqPPM better quality
- Diffuse Torus in glassPT Bad qualityBDPT again good on caustics on diffuseBad on SDSGood results in PPM
- Reflection of causticsNot efficiently possible with MC