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SoftShadowsMichael Schwarz
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HardShadows
Completely lit
Umbra
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SoftShadows
Completely lit
Umbra
Penumbra
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ShadowHardeningonContact
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Shadowing=Point–RegionVisibility
Task:For each receiver sample (point),
determine visible fractionof light source (region)
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OccluderFusion
Occlusion : 30% Occlusion : 70% Total occlusion: 90%
∑ : 100%
max : 70%
1 ∏ 1 : 79%
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OccluderFusion
Occlusion : 30% Occlusion : 70% Total occlusion: 100%
∑ : 100%
max : 70%
1 ∏ 1 : 79%
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OccluderFusion
Occlusion : 30% Occlusion : 70% Total occlusion: 70%
∑ : 100%
max : 70%
1 ∏ 1 : 79%
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OccluderFusion
Occlusion : 30% Occlusion : 70% Total occlusion: 79%
∑ : 100%
max : 70%
1 ∏ 1 : 79%
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SoftShadowsImage‐basedSolutions
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BlurringofHardShadowTestResults
Use any of the filtering approaches presented before Yields a soft‐shadow‐like appearance
But: Ignores varyingpenumbra width
VSM, 512×512, 62×62
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BlurringofHardShadowTestResults
Use any of the filtering approaches presented before Yields a soft‐shadow‐like appearance
But: Ignores varyingpenumbra width
Reference
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PCSS
BlurringofHardShadowTestResults
Idea: Choose blur kernel size adaptively But how?
Percentage‐CloserSoft Shadows (PCSS)[Fernando, 2005]
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Percentage‐CloserSoftShadows
Average occluder depth
Shadow map
1. Blocker search
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Percentage‐CloserSoftShadows
1. Blocker search2. Penumbra width estimation
Planar occluder
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Percentage‐CloserSoftShadows
1. Blocker search2. Penumbra width estimation3. Filtering
Filter region(size ~ )
50%
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Percentage‐CloserSoftShadows
1. Blocker search2. Penumbra width estimation3. Filtering
Two of these three steps requiremany shadow map accesses!
Acceleration approaches Subsampling Prefiltering
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Prefiltering
Filtering step = Percentage‐closer filtering Alternative representation (VSM, CSM, ESM) allows prefiltering Blurring reduces to accessing a mipmap/N‐buffers/summed‐area table
Blocker search can also be sped up with prefiltering [Annen et al., 2008]
Averaging the depth of shadow map samples closer to the lightcan be expressed as a convolution
Hence approach analogous to CSM is possible (“CSM‐Z”) Examples: Convolution Soft Shadows,
Exponential Soft Shadow Mapping [Shen et al., 2013]
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VarianceSoftShadowMapping
Estimates average occluder depth directly from prefiltered VSM Expression involves Chebyshev inequality
Problem: Chebyshev inequality may not be applicable Happens if average depth in query region is larger than receiver depth Conservatively assuming receiver point is fully lit often leads to artifacts
Improvement: subdivide query region and recurse on subregions If region small and Chebyshevinequality still not applicable:use 2×2 PCF
[Yang,Dongetal.,2010]
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High‐QualityFiltering
PCF: samples weighted equally Analytic: actual coverage[Shen et al., 2011]
24% 40% 40% 40% 8%
36% 60% 60% 60% 12%
60% 100% 100% 100% 20%
60% 100% 100% 100% 20%
60% 100% 100% 100% 20%
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AnisotropicFilterKernels
[Shen et al., 2013][Shen et al., 2011]
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PredictedVirtualSoftShadowMaps
Adaptive shadow‐map partitioning Guided by perceptual resolution prediction metric Accounts for screen‐space frequency of penumbrae
[Shen etal.,2011]
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Percentage‐CloserSoftShadows
Reference
Umbra too small
Penumbra too large
Shifted umbra center
Main sources of incorrectness Single planar occluder assumption
Classification as light blockingsolely based on depth test
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Percentage‐CloserSoftShadows
Main sources of incorrectness Single planar occluder assumption
Classification as light blockingsolely based on depth test
Samples actually occluding p
Samples assumedto occlude p
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Percentage‐CloserSoftShadows
+ Simple and reasonably fast
+ Often visually pleasing results(at least for smaller light sources)
− Not really physically plausible
− Only accounts for occluders visiblefrom light source’s center
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VisibilityComputation=Filtering
Rectangular light source
Planar occluder parallel to light Represented by blocker image
Visibility factor is obtained via boxfiltering the blocker image Filter size equals appropriately scaledlight size
Box filter region
[Soler andSillion,1998]
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VisibilityComputation=Filtering
Advantages Enables prefiltering Query becomes constant‐time(independent of light size)
Blocker image Box‐filteredblocker image
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OcclusionTextures
Decompose scene into multiple planar layers Slice scene parallel to light source Project geometry within slice onto slice’s bottom plane
[Eisemann andDécoret,2006]
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OcclusionTextures
Decompose scene into multiple planar layers Slice scene parallel to light source Project geometry within slice onto slice’s bottom plane
Covered parts of each sliceare encoded in a binaryocclusion texture (= blocker image)
[Eisemann andDécoret,2006]
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OcclusionTextures
Prefilter occlusion textures
At each view sample For each blocking slice,lookup appropriately filtered responsein prefiltered occlusion texture
Accumulate per‐sliceshadow contributions
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OcclusionTextures
+ Plausible soft shadows at high frame rates
+ Performance independent of light size
Mainly suited for compactindoor environments
− Heuristic occluder fusion
− Discretizes occluders intosmall number of perforated planes
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Occluder Backprojection
For each (relevant) occluder Project it onto light source Determine covered light area Aggregate this occlusion information
GatheringFor all receiver pointsFor all potential occluders
ScatteringFor all occludersFor all affected receiver points
Receiver point
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SoftShadowMapping
Approximate (subset of) occluder geometry
Generate shadow map(from light’s center)
[Attyetal.,2006;Guennebaudetal.,2006]
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SoftShadowMapping
Approximate (subset of) occluder geometry
Generate shadow map(from light’s center)
Derive occluder approximationby unprojecting texels into world space Micropatches
Micropatch
[Attyetal.,2006;Guennebaudetal.,2006]
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SoftShadowMapping
Backproject micropatches ontolight source to determine visibility
Simple approach:Sum up projections’ covered areas Ignores overlaps
Receiver pointMicropatch
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SoftShadowMapping
Backproject micropatches ontolight source to determine visibility
Simple approach:Sum up projections’ covered areas Ignores overlaps
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SoftShadowMapping
Backproject micropatches ontolight source to determine visibility
Simple approach:Sum up projections’ covered areas Ignores overlaps
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SoftShadowMapping
Backproject micropatches ontolight source to determine visibility
Simple approach:Sum up projections’ covered areas Ignores overlaps
Overlap
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OverlappingArtifacts
Reference Area accumulation(without accounting for overlaps)
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OverlappingArtifacts
Reference Occlusion bitmasks(with correct overlap handling)
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OcclusionBitmasks
Sample visibility instead of accumulating areas Set of binary point‐to‐point visibility relations Bit field is employed to track visibilitiesof sample points on light source
Light sample point
[Schwarz&Stamminger,2007]
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OcclusionBitmasks
Sample visibility instead of accumulating areas Set of binary point‐to‐point visibility relations Bit field is employed to track visibilitiesof sample points on light source
[Schwarz&Stamminger,2007]
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OcclusionBitmasks
Sample visibility instead of accumulating areas Set of binary point‐to‐point visibility relations Bit field is employed to track visibilitiesof sample points on light source
[Schwarz&Stamminger,2007]
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OcclusionBitmasks
Sample visibility instead of accumulating areas Set of binary point‐to‐point visibility relations Bit field is employed to track visibilitiesof sample points on light source
[Schwarz&Stamminger,2007]
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OcclusionBitmasks
Sample visibility instead of accumulating areas Set of binary point‐to‐point visibility relations Bit field is employed to track visibilitiesof sample points on light source
16×16 jittered light sample
points
[Schwarz&Stamminger,2007]
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ManyOptions
Visibility determination methods Area accumulation Occlusion bitmasks
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ManyOptions
Visibility determination methods
Occluder approximations Micropatches Microquads (and microtris) Occluder contours
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ManyOptions
Visibility determination methods
Occluder approximations
Acceleration by adapting accuracy Micro‐occluder subsampling Coarser occluder approximations Subsampling in screen space
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ManyOptions
Visibility determination methods
Occluder approximations
Acceleration by adapting accuracy
Acceleration via multi‐scale (min/max) representations Search area pruning Direct identification of umbra and fully‐lit receiver points Hierarchical occluder construction
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ManyOptions
Visibility determination methods
Occluder approximations
Acceleration by adapting accuracy
Acceleration via multi‐scale (min/max) representations
Practical example: variant by Geomerics [Martin, 2012]
Targets large area lights CUDA‐based implementation
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SoftShadowMapping
+ Physically plausible
+ Rather high qualityat real‐time frame rates possible
Performance strongly dependent on Number of pixels requiring backprojection Search area size (number of backprojected texels)
− Uses only approximation of subset of occluders Typically those visible from the light source’s center