09 Shadow Mapping Steve Marschner CS5625 Spring 2019 Thanks to - - PowerPoint PPT Presentation

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09 Shadow Mapping Steve Marschner CS5625 Spring 2019 Thanks to - - PowerPoint PPT Presentation

Nov 14, 2023 95 likes •486 views

09 Shadow Mapping Steve Marschner CS5625 Spring 2019 Thanks to previous instructor Kavita Bala Shadows as depth cue [tricks-and-illusions.com] Shadows as anchors Shadows as anchors [Mller et al. RTR ] Mark Kilgard Mark Kilgard Mark

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09 Shadow Mapping

Steve Marschner CS5625 Spring 2019

Thanks to previous instructor Kavita Bala

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Shadows as depth cue

[tricks-and-illusions.com]

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Shadows as anchors

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Shadows as anchors

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[Möller et al. RTR]

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Mark Kilgard

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Mark Kilgard

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Mark Kilgard

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Mark Kilgard

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slide courtesy of Kavita Bala, Cornell University

Shadow Map Issues

  • if A and B are approximately equal?
  • Speckling
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Mark Kilgard

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Mark Kilgard

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Mark Kilgard

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  • pengl-tutorial.org

first try at shadow mapping

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Mark Kilgard not enough shadow bias good shadow bias too much shadow bias

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  • pengl-tutorial.org

shadow mapping with constant bias

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  • pengl-tutorial.org

shadow mapping with slope-dependent bias

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  • pengl-tutorial.org

closed surfaces and slope-dependent bias

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  • pengl-tutorial.org

adding percentage-closer filtering

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Shadow map sample rate—bad case

Light behind object Light’s “view direction” almost


  • pposite the eye’s view


direction “Dueling frusta”

Mark Kilgard eye view light view

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Cascaded shadow maps (aka. parallel-split SM)

[Möller et al. RTR]

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Single shadow map, 2048x2048 Four 1024x1024 shadow maps (equal memory)

Fan Zhang, Chinese U. Hong Kong

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Filtering shadow maps

Shadow map lookups cause aliasing, need filtering As with normal maps, pixel is a nonlinear function of the shadow depth

  • this means applying a linear filter to the depth is wrong

We want to filter the output, not the input, of the shadow test

  • what fraction of samples pass the test
  • samples pass the test if they are closer than the shadow map depth
  • therefore “percentage closer filtering” or PCF
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Kavita Bala, Computer Science, Cornell University

Percentage Closer Filtering

  • Soften the shadow to decrease aliasing

– Reeves, Salesin, Cook 87 – GPU Gems, Chapter 11

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Kavita Bala, Computer Science, Cornell University

1 sample SM

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Kavita Bala, Computer Science, Cornell University

4 sample PCF

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Kavita Bala, Computer Science, Cornell University

16 sample PCF

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Kavita Bala, Computer Science, Cornell University

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Soft shadows from small sources

Main effect is to blur shadow boundaries

  • PCF can do this
  • …but how wide to make the filter?

Real shadows depend on area of light visible from surface

  • this can vary in complex ways
  • example: sun viewed through leafy trees

Useful approximation: convolution

  • shadows are convolutions when the blocker and source are parallel and planar
  • occluder fusion: approximating some occluding geometry as a planar blocker
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Hard Shadows

Completely lit Umbra

Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course

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Soft Shadows

Completely lit Umbra Penumbra

Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course

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Shadow Hardening on Contact

Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course

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Percentage‐Closer Soft Shadows

Average occluder depth

  • Shadow map
  • 1. Blocker search

Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course

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Percentage‐Closer Soft Shadows

  • 1. Blocker search
  • 2. Penumbra width estimation

Planar

  • ccluder
  • Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course
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Percentage‐Closer Soft Shadows

  • 1. Blocker search
  • 2. Penumbra width estimation
  • 3. Filtering
  • Filter region

(size ~ ) 50%

Michael Schwarz, SIGGRAPH 2013 Real Time Shadows course

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Percentage-closer soft shadows

Fernando, NVidia whitepaper ~2005