Heterogeneous Isotropic BRDFs. Homogeneous BRDF. Spatially Varying BRDF. Approaches - Sampling. Approaches - Illumination

Transcription

1 Couse 0 Realistic Mateials in Compute Gaphics Heteogeneous Isotopic s Henik P.A. Lensch Stanfo Univesity Homogeneous Spatially Vaying Appoaches - Sampling Appoaches - Illumination ense sampling fo each texel Reflectance Fiels, BTF spase sampling image-base Measuement combining samples fom iffeent suface points spatial vaiation constant specula pat vs. clustee s point light contolle conition inteeflections most often neglecte envionment maps still iect illumination only global invese illumination

2 Measuement Image-Base Measuement Gonioeflectomete senso [Maschne 999, Lu & Koeneink 998, ] camea light souce captue lots of samples at one shot by a senso aay / camea sample homogeneous, isotopic mateials only cuve sample Setup Camea an light souce ae move manually aoun the object. Positions ae calibate with espect to the object. The ak oom euces eflections fom the envionment. Captue HDR-images fom vaious viewpoints with iffeent light souce positions. Captue HDR-images fom vaious viewpoints with iffeent light souce positions. 2

3 3D-2D calibate ganty coesponing points silhouette-base metho Light Souce Position Light Souce Position etect highlights of ing flash eflections etect highlights of light souce eflections etemine the position of the sphees econstuct light souce position Light Souce Position 3

4 x fo each point on the suface: fin all images whee the point is visible an lit take sample at coesponing pixel position (, o x, ωi, ω ) Key Iea The Lafotune Moel Vey few aiance samples pe texel no ense sampling of the N j ( C ( ω ω + ω ω C ω ) f ˆ ˆ = + ω ( ω i, ωo) ρ + x, j i x o x i y o ) y z, j j i z o z Most eal-wol objects consist of a small set of istinct mateials. fit a moel fo each basis mateial stat with the avg. of the entie suface physically plausible iffuse component plus a numbe of lobes 3*(+ i*3) paametes (2 fo a single lobe moel) fit paametes to samples using Levenbeg- Maquat s to Lumitexels Result efine eo measue between a an a lumitexel: E ( L) = f i j o j i j z j L R j L = aveage eo ove all aiance samples pefom non-linea least squae optimization fo a set of lumitexels using Levenbeg-Maquat yiels a single (i.e. its paametes) pe set of lumitexels ( f ( ω, ω ) ω ) 2 4

5 Split-Recluste-Fit Cycle Goal: sepaate the iffeent mateials simila to Lloy iteation cluste 2 split ecluste fit stat with a single cluste containing all lumitexels split cluste along iection of lagest vaiance stop afte n clustes have been constucte split into two s along iection of lagest vaiance of paametes (covaiance matix) istibute initial lumitexels foming two new clustes efit new s epeat eclusteing an fitting until clustes ae stable Results Spatially Vaying Mateials Pojection Pojection Goal: assign a sepaate to each lumitexel too few aiance samples fo a eliable fit compute the pseuo-invese using non-negative SVD to get a least squaes solution fo epesent the f of evey lumitexel by a linea π combination of aleay etemine s of the clustes f, f2, K, f m : f π = t f + t2 f2 + K+ t m f m etemine linea weights t,, t 2, K t m f(, ˆ ωo i, z 2 f(, ˆ ωo 2 2 i 2, z = M M f L (, ˆ ω L o L i L, z i 2 M f ( ˆ ω, ˆ ω i L it is a linea poblem! f2(, ˆ ω o f ( ˆ ω, ˆ ω 2 2 o2 o L i, z i 2, z i L, z L L O L fm(, ˆ ω o f ( ˆ ω, ˆ ω m m i 2 M f ( ˆ ω, ˆ ω i L o2 o L i, z i 2, z i L, z t t2 M tm 5

6 Results Why to o the complicate clusteing? Nomal Without Nomal With Nomal Bonze Buste 6

7 Moel Exhibition fo Cultual Heitage Applications Conclusion Pesevation, ocumentation, estoation, communication, accessibility etemine of a few basis mateials spatial vaiation as a blen of basis s highly efficient acquisition moel base equies geomety moel 7