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Image Based Lighting |
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Image-Based BRDF measurement |
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Inverse Global Illumination |
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Allows us to place 3D objects into photos of
real scenes. |
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Create accurate interactions with 3D objects
placed in a scene. |
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Capturing real-world illumination as an
omnidirectional, high dynamic range image |
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Mapping the illumination onto a representation
of the environment |
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Placing the 3D object inside the environment |
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Simulating the light from the environment
illuminating the computer graphics object |
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Used as the captured lighting input for the IBL |
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Created from multiple images to give an exacting
account for all the light in the scene |
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These images where constructed from two radiance
images of a mirrored sphere |
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single high dynamic range images of a mirrored
ball |
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the images show the camera and the photographer |
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not well sampled in the area that is opposite
the camera. |
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“dynamic range” of a scene is the contrast ratio
between the brightest and darkest parts |
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A HDR image has a greater dynamic range than
shown on a standard device |
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HDR images has pixel values proportional to the
amount of light in the world corresponding to the pixel |
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HDR images are typically generated by combining
multiple normal images of the same scene with different light intensities |
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A Light Probe Image is mapped to a large sphere
surrounding the model |
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When a ray hits the IBL environment it takes on
the pixel value of the corresponding
point in the light probe image. |
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Scene Rendered using Radiance before insertion
of the image for environmental lighting |
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These images are real objects with captured
environmental light illuminating them |
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Done by taking a large set of images of the
object as illuminated by all possible directions |
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Linear combination of the images can produce
images under arbitrary lighting conditions |
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The IBL environment determines the combination
of the images |
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http://www.debevec.org/Research/IBL/ |
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Can measure the BRDF of a material without a
Gonioreflectometer |
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Uses fewer measurements to define the BRDF than
a Gonioreflectometer |
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Less expensive than a Gonioreflectometer |
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Draw Back: Can only perform measurements on
surfaces which can be placed on the geometry of a physical sphere |
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Coatings |
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Sheet of flexible material |
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Fixed Position Primary Camera |
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Light Source |
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Secondary Camera for position measurement |
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Sample: Sphere painted with various coatings or
a sheet of flexible material |
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Photometric targets on the sample container |
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Illuminate the sample from a sequence of known
positions |
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Finds the position of the light source using the
second camera |
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32 measurement images from the primary camera |
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96 when three filters are used for RGB |
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32 light source calibration images from the
second camera |
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Primary camera is in a known fixed position |
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Secondary camera is aimed at the sample and
mounted below the light source to image the photometric targets |
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Targets are mounted on the base on the sample |
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Targets have a known 3D position relative to the
sample and the primary camera |
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By analyzing the measurement image the position
of the secondary camera and thus the light source can be determined |
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This method is accurate to a few millimeters |
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For each pixel in the primary camera image
determine the surface point and the normal |
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The direction of illumination is computed
relative to the surface point and the normal |
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Compute the relative irradiance from the known
source geometry |
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Compute the BRDF by dividing the radiance (pixel
value) by the irradiance |
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The BRDF measured shows reciprocity when mapped
as a height field over the (θi, θe) plane |
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Goal: To model a scene with a realistic
reflectance properties, from images of the scene, which can be given novel
lighting conditions or have 3D objects placed in it. |
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Estimates the incident radiances of the surfaces
in a scene. |
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Radiance estimate used to estimate the
reflectance properties of the surfaces in the scene, by an iterative
procedure |
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Reflectance property estimates can then be used
re-estimate the incident radiances |
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The surfaces of the environment are broken into
a finite number of patches |
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Patches assumed to have constant radiosity and
diffuse albedo |
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For each patch: |
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Bi = Ei + ρiΣjBjFij |
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Bi is the radiosity |
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Ei is the emission |
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ρi is the diffuse albedo |
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Fij is the form factor between the
patches |
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The form factor is the total power leaving patch
i that is received by patch j |
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Bi and Ei are measured
from a photo with known geometry. Fij is derived from the
geometry. So we can find the
diffuse portion of the reflection: |
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ρi = (Bi -
Ei)/(ΣjBjFij) |
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Li = (ρd/π + ρsK(α,Θi))Ii |
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Li is the radiance |
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Ii is the irradiance |
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ρd/π is the diffuse term |
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ρsK(α,Θi)
is the specular term |
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α is the parameterized surface roughness |
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Θi is the azimuth of the
incident and viewing directions |
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The data from the images collected we can solve
the nonlinear optimization problem and get parameters for ρs,
ρd and α |
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The radiance image must cover an area with a
specular highlight or we will not have enough information for recovering
the specular parameters. |
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Debevec P., “Image-Based Lighting”, IEEE
Computer Graphics and Applications March/April 2002, pp. 26-34 |
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Yu, Debevec, Malik, Hawkins, "Inverse
Global Illumination: Recovering Reflectance Models of Real Scenes from
Photographs", Proc. ACM SIGGRAPH 1999 |
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Marschner S.R., Westin S.H., Lafortune P.F., and
Torrance K.E., "Image-Based Bidirectional Reflectance Distribution
Measurement", Applied Optics 39: 16, 2000 |
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Notes