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Recovering High Dynamic Range Radiance Maps from Photographs

Recovering High Dynamic Range Radiance Maps from Photographs. Paul E. Debevec, Jitendra Malik. In SIGGRAPH 97, August 1997. Introduction. “Dynamic Range” of a scene is the contrast ratio (brightest / darkest parts) Recovering High Dynamic Range Radiance Maps Multiple Photographs

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Recovering High Dynamic Range Radiance Maps from Photographs

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  1. Recovering High Dynamic Range Radiance Maps from Photographs Paul E. Debevec, Jitendra Malik. In SIGGRAPH 97, August 1997

  2. Introduction • “Dynamic Range” of a scene is the contrast ratio (brightest / darkest parts) • Recovering High Dynamic Range Radiance Maps • Multiple Photographs • Recover … Response function ( film response in exposure ) • Differently Exposed Photographs • Image-Based Modeling & Rendering • Image – same ( exposure setting , film response function ) • Recovering reflection models ( BRDF ) • require … absolute Radiance Values

  3. Image Acquisition Pipeline

  4. Film Response Recovery • The response of a film Optical Density of film against the Exposure digital number Z ( development , scanning , digitization process ) Input – Z ,

  5. Film Response Recovery • To complete description - ( unit exposure) - weighting function - pixel location ( Zmin ~ Zmax , image )

  6. Constructing the HDR Radiance Maps • Curve - Radiance values ( associated with g ) Combining the multiple exposures - reduce Noise, artifacts ( such as film grain )

  7. Constructing the HDR Radiance Map • Storage • Map  image format

  8. Rendering Synthetic Objects into Real Scene: Bridging Traditional and Image-Based Graphics with Global Illumination and High Dynamic Range Photography Paul E. Debevec,. In SIGGRAPH 98, July 1998

  9. Introduction • Scene Radiance & Global Illumination • High Dynamic Range Image-based model • Illuminate the NEW objects • 3 components • Distant Scene • Local Scene • Synthetic Objects

  10. Introduction • Light Probe • HDR Panoramic Radiance Map ( near rendered location ) • Light-Based Model

  11. Illuminating Synthetic Objects with Real Light • Accurately Recording Light in a scene • Large areas of Indirect Light • Concentrated areas of Direct Light • Recovering HDR • Measure of Scene Radiance

  12. The General Method • Distant Scene ( a Light-Based Model ) • Light-Based Model • Accurate measure of Incident Illumination • ( in vicinity of objects, desired viewpoint ) • Local Scene ( approximate Material-Based Model ) • Interact with the Synthetic Objects • Geometry, Reflectance characteristic • Synthetic Object • variety of Shapes , Materials

  13. Compositing using a Light Probe • Constructing a Light-Based Model of real scene • Fully Dynamic Range Omnidirectional Radiance Map • Radiance measurement  mapped ( geometry of Distant Scene ) • Mapping from the Probe to the scene model • Mapping coordinates ( ball , world ) • Position, Size, camera Parameter ( location in scene, focal length ) • Assume … Small , Orthograph  good approximation • Creating Rendering

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