Monte Carlo methods for volumetric light transport ...wjarosz/publications/novak18monte-slide… ·...
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Monte Carlo methods for volumetric light transportMonte Carlo methods for volumetric light transportsimulationsimulation STAR at EG 2018STAR at EG 2018 Advanced methods and acceleration data structuresAdvanced methods and acceleration data structures
Johannes Hanika Lehrstuhl für Computergrafik Karlsruhe Institute of Technology
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contentscontentsselection of advanced methods:
Dwivedi sampling / zero variance random walksspectral tracking
acceleration data structuresfor regular trackinginside these: null collision based
emissive media
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Dwivedi samplingDwivedi samplingregular path tracing random walk
tends to get lost inside a volume bounded by a shape
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Dwivedi samplingDwivedi samplingrandom walk biased to exit bounded volume as quickly as possible
assumes constant illumination from the outside [Kd14]
assume homogeneous slab with isotropic phase functionapproximate closed-form solution of transport using this simplified setting
known as zero-variance theory (term may be a bit bold)
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Dwivedi samplingDwivedi samplingrandom walk biased towards light source
aims to exit towards light [MHD16]
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Dwivedi samplingDwivedi samplingrandom walk biased towards light source
aims to exit towards light [MHD16]
achieved by biasing the PDF to sample direction and distanceestimator remains unbiased!
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spectral trackingspectral trackinganother problem with skin: chromatic media
collision coefficients depend on wavelength for instance free flight distance much longer for long wavelengths:
makes path invalid for different wavelength?can we still exploit coherence?
μ λ
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spectral tracking via MISspectral tracking via MIShero wavelength sampling [WND∗14]
450 500 550 600 650 700
450 500 550 600 650 700
sample perfectly for one single wavelength evaluate path for a stratified set of wavelengths at the same timeoptimally weighted combination via MIS (balance heuristic)
limited to regular tracking because it requires explicit evaluation of PDF
λ0
λi
f( , )x̄ λi
p( , )∑j x̄ λj
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spectral tracking via MISspectral tracking via MISimage comparison 64sppimage comparison 64spp
skin material with 1 wavelength
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spectral tracking via MISspectral tracking via MISimage comparison 64sppimage comparison 64spp
skin material with 4 wavelengths (SSE)
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spectral tracking via MISspectral tracking via MISimage comparison 64sppimage comparison 64spp
skin material with 8 wavelengths (AVX)
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spectral tracking without PDF [KHLN17]spectral tracking without PDF [KHLN17]sample by common majorant how do decide for null collision, scattering, or absorption?probability according to , ,
pick by maximum over pick by average weighted by spectral path throughput history
0.0 0.2 0.4 0.6 0.8 1.0
distance
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extinction
µ.redµ.greenµ.blue
RGBmedium
0.0 0.2 0.4 0.6 0.8 1.0
distance0
1
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5 throughput.redthroughput.greenthroughput.blue
Maximum
0.0 0.2 0.4 0.6 0.8 1.0
distance0
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max throughput
throughput.redthroughput.greenthroughput.blue
History-aware avg.
μ̄
(λ)μn (λ)μs (λ)μa
λi
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spectral tracking without PDF [KHLN17]spectral tracking without PDF [KHLN17]sample by common majorant how do decide for null collision, scattering, or absorption?probability according to , ,
pick by maximum over pick by average weighted by spectral path throughput historyresults in different noise patterns:
μ̄
(λ)μn (λ)μs (λ)μa
λi
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speed!speed!low variance estimators are importantlow variance estimators are important
but also, in volumes most of the run time is memory fetching
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acceleration data structuresacceleration data structuresgrid, super voxels [SKTM11], kd-tree [YIC∗11], adaptive blocks
adaptivity driven bypixel footprint / camera tessellationheterogeneity / variation
two-level modelling (super voxel, kd nodes) store majorants in coarse blocksμ̄
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acceleration data structuresacceleration data structuresgrid, super voxels [SKTM11], kd-tree [YIC∗11], adaptive blocks
adaptivity driven bypixel footprint / camera tessellationheterogeneity / variation
two-level modelling (super voxel, kd nodes) store majorants in coarse blocksperform regular tracking on coarse blocks [SKTM11]
μ̄
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acceleration data structuresacceleration data structuresgrid, super voxels [SKTM11], kd-tree [YIC∗11], adaptive blocks
adaptivity driven bypixel footprint / camera tessellationheterogeneity / variation
two-level modelling (super voxel, kd nodes) store majorants in coarse blocksperform regular tracking on coarse blocks [SKTM11]access on fine levels to sample collision type
μ̄
(λ), (λ)μs μa
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acceleration data structuresacceleration data structuresregular trackingregular tracking
needs to step through every voxel, bad for fine tessellationswell chosen tessellation is a big advantage!
distance
dens
ity
distance
dens
ity
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acceleration data structuresacceleration data structuresnull collision-based trackingnull collision-based tracking
is independent of tessellation and is efficient in thin media (few events)high number of events in dense media, regardless of tessellation!
distance
dens
ity
distance
dens
ityaccessing the memory within the same voxel is still expensivealleviated by decomposition tracking [KHLN17]
separate into sum of coarse and fine, to sample distance pick shortest (and early out!)also profits full regular tracking
μ
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emissive mediaemissive mediathin/dense media make a differencethin/dense media make a difference
no event inside the medium means we cannot pick up emission:
density
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emissive mediaemissive mediathin/dense media make a differencethin/dense media make a difference
following the idea of beams, collect emission along a ray
particularly well suited for regular tracking, touching all voxels anyways
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emissive mediaemissive mediathin/dense media make a differencethin/dense media make a difference
direct application of MIS with NEE [VH13] introduces noise: Point + NEE Line + NEE
reason: NEE cannot create paths with end point outside the mediumforward scattering PDF is poor, however, and now it picks up line emision!
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emissive mediaemissive mediathin/dense media make a differencethin/dense media make a difference
need to teach next event estimation about line emission [SHZD17]: Point + NEE Line + NEE Line + FNEE
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end of contentend of contentup next:up next:summary and open research problems
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summarysummaryfree flight distance samplingfree flight distance sampling
woodcock/delta trackingtransmittance estimationtransmittance estimation
track-lengthresidual ratiofree flight versions
path samplingpath samplingpath space formulationsummary of advanced methods
acceleration structuresacceleration structuresfor regular trackingfor null collisions (bottom-level)
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open research problemsopen research problemsnull collision algorithms and MISnull collision algorithms and MIS
missing link to integrate into powerful frameworkfor instance combine with equi-angular sampling
can we estimate the PDF?
expectation and division do not commute!
X =f( )x̄
p( )x̄
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open research problemsopen research problemsleverage recent advances in machine learningleverage recent advances in machine learning
special purpose denoisingincluding a volume prior?
path guiding for volumes?importance sampling for multiple vertices?
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open research problemsopen research problemsjoint handling of surfaces and geometryjoint handling of surfaces and geometry
still o�en surface transport is handled separatelymakes inclusion of all interreflections hardcustom-cut algorithms increase maintenance cost
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open research problemsopen research problemsgeneralisation to correlated scatterersgeneralisation to correlated scatterers
core assumption of exponential path length: uncorrelated particles!particle repulsion such as in cell growth is very correlatedreally, no collision can be found inside the current particle (min distance)some existing work
[d'Eon 2018, Bitterli et al. 2018]
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thank you!thank you!any questions?any questions?
acknowledgements:acknowledgements:Peter Kutz
for tracing down many of early delta tracking papersJaroslav Křivánek & reviewers
for feedback on the paper dra�Maurizio Nitti
for help w/ fast forward and illustrations
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