Progressive photon mapping
- 1. Progressive Photon MappingT. Hachisuka, S. Ogaki, H.W. Jensen
- 2. Monte CarloPhoton MappingProgressive Photon MappingResults
- 3. Monte Carlo TechniquesPath TracingLight TracingBidirectional Path TracingMetropolis Light Transport
- 4. Caustics
- 10. Lights in Glass
- 11. Water
- 12. Swimming Pool
- 13. Monte CarloPhoton MappingProgressive Photon MappingResults
- 14. SDS Paths
- 15. Photon Mapping
- 19. Weakness
- 20. Quality is Memory Limited
- 21. Monte CarloPhoton MappingProgressive Photon MappingResults
- 22. Photon MappingPhoton TracingRay Tracing
- 23. Reverse Photon ShootingRay TracingPhoton Tracing
- 26. Progressive Photon MappingRay TracingPhoton Tracing
- 30. Radius ReductionAssume homogeneous density𝑑=𝑁+𝑀𝜋𝑅 𝑁=𝜋𝑅2𝑑
- 31. Radius ReductionConsistency requirement:𝑁>𝑁⇒𝑁=𝑁+𝛼𝑀New Radius:⇒𝑅=R𝑁+𝛼𝑀 𝑁+𝑀
- 32. Flux Correction𝜏𝑁 ≠ 𝜏𝑁 + 𝜏𝑀Assume homogeneous illumination𝜏𝑁=𝜏𝑁 + 𝜏𝑀𝜋𝑅𝜋𝑅=𝜏𝑁 + 𝜏𝑀𝑁+𝛼𝑀𝑁+𝑀
- 33. OverviewRaytracing firstMultiple photon tracing passesRadius reductionFlux correction
- 34. Monte CarloPhoton MappingProgressive Photon MappingResults
- 35. Results
Editor's Notes
- #2: Introduce yourselfProgressive Photon MappingPaper by Hachisuka etal
- #3: Related Techniques – Monte Carlo + Photon Mapping + ProblemsProgressive Photon MappingResults
- #4: All share a similar problem
- #5: Inefficient for CausticsEspecially classic path tracingRecap: What are caustics?
- #6: ViewerLightsourceBlue -> DiffuseGreen -> Specular
- #7: SD PathsFrom Light To SpecularTo DiffuseTo CameraProblem: Tracing from ViewerSample SemisphereRed path very improbable
- #8: Easy way to solve problemStart tracing at lightDiffuse surface -> Force ray to camera -> no samplingOther caustics related path problematic
- #9: Instead of Camera -> DiffuseCamera -> Specular -> Diffuse
- #10: Cannot force ray to cameraRed path highly improbableSlowly convergingReal life examples?
- #11: First specular bounce: Light in glassSpecular reflections in scene very inefficient
- #12: Water in a swimming poolCaustic effects on bottom of poolSchematic
- #13: Light enters -> specularCaustics on ground -> diffuseSeen through surface -> specularSame setup as before
- #14: More efficient solution: Photon MappingExplain PM for special case:SDS paths
- #15: Same setupSDS PathSimilar as Light TracingStart at lightsource
- #16: Trace photons into sceneBounce at specular surfaceHit diffuse surface
- #17: At diffuse surface: STORE3D structure like KD-TreeTrace millions of photonsAccess them in following pass
- #18: RaytracingShoot rays into sceneBounce on specular surfaceOn diffuse surface: Estimate Photon Density
- #19: 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
- #20: Quality <- # photonsStore all photons-> #photons limited by memory
- #21: PM does not allow arbitrary precision MCPT: Calculate longer -> better qualityPM: Hardware limits quality
- #22: PPM makes a few changes to PM
- #23: Photon Tracing pass-> Store all photonsRay Tracing pass-> Estimate IlluminationFirst Change:
- #24: Turn around.First Ray Tracing, then Photon Tracing.How does this work?
- #25: Same algorithm as ray tracing in non-progressiveStore all hit points (KD-Tree)-> Structure of all visible points
- #26: Then shoot photons. Store all photonsThen estimate illumination of each hitpointAdvantage for Progressive Photon Mapping
- #27: We can do multiple passes of photon tracingWith just one ray tracing pass.Progressively enhance qualityWithout storing photonsNot trival
- #28: 2 passes of photon tracingEstimate photon density of accumulation Second pass will yield similar radius
- #29: When merging both casesK-Nearestneighbours should return smaller radius
- #30: Represent growing frequencyRadius converge to zeroProblem: N-Nearest neighbors not possibleWe need an estimate
- #31: Assume homogeneousDetermine densityMultiply with new area to get number of photonsDetermine R
- #32: For consistencyConverge to right solutionAdd photons in every iteration-> new parameter alpha-> fraction of photons to keepKnow number of photons -> determine radius
- #33: Another problem:Accumulate flux every iterationFlux depends on area. Cannot simply add flux, correct by ratio of areas
- #35: More efficient solution: Photon MappingExplain PM for special case:SDS paths
- #36: PT badLT good on diffuse surfaceBad on specular -> cameraPM good, but low freqPPM better quality
- #37: Diffuse Torus in glassPT Bad qualityBDPT again good on caustics on diffuseBad on SDSGood results in PPM
- #38: Reflection of causticsNot efficiently possible with MC