Is Spectral Lighting Environment Necessary for Photorealistic Rendering?
Takahiro Okabe∗
Kyushu Institute of Technology

Megumi Miura
Kyushu Institute of Technology

Imari Sato
National Institute of Informatics

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Intensity

151050400 450 500 550 600 650 700
Wavelength200

Intensity

150
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Wavelength

Figure 1: The spectral lighting environment of a real scene acquired by using a hyperspectral camera and a mirrored ball.

1

Motivation and Contribution

The appearance of an object depends not only on the geometric andphotometric properties of the object but also on the light sourcesilluminating it. The technique that measures the omnidirectional
lighting environment of a real scene and uses it for photorealistic image synthesis is called Image-Based Lighting (IBL) [Debevec
1998; Sato et al. 1999].
In conventional IBL, a 3-band RGB camera is used for measuring the lighting environment of a scene. Unfortunately, however,the spectral resolution of 3-band cameras is low, and therefore wecannot prevent the occurrence of so-called metamerism in general.
Specifically, when two light sources with different spectral distributions are observed by using a 3-band camera, the apparent RGB
values of those light sources could be the same. It is also shownthat spectral rendering is of critical importance for subsurface scattering, dispersion, and volumetric effects [Wilkie et al. 2014].
Accordingly, in this study, we acquire the omnidirectional lighting
environment of a real scene by using a hyperspectral camera, which
has several tens of bands in visible spectrum, and then use the acquired spectral lighting environment for image synthesis. In particular, we compare the images synthesized by using the spectral
lighting environment with those using the RGB lighting environment, and evaluate the difference between them. We demonstratethat spectral lighting environment is not necessarily important for
rendering reflective materials, but is highly important for rendering
fluorescent materials, which are common and present in 20 percent
of randomly constructed scenes [Barnard 1999].

2

Highlight of Experimental Results

The HDR spectral lighting environment of a real scene was acquired from an image sequence of a mirrored ball with uniform
reflectance in visible spectrum. Those images were captured by using a hyperspectral camera from EBA Japan, which has a samplinginterval of 5 nm. The light source spectrum from each incident direction to the mirrored ball is obtained as shown in Figure 1.
Figure 2 shows the images of 64 fluorescent spheres with different∗ okabe@ai.kyutech.ac.jp

Figure 2: The images of 64 fluorescent spheres with different absorption and emission spectra synthesized by using the acquired
spectral (top left) and RGB (top right) lighting environments. The
RMS differences between the synthesized images in CIE-L*a*b*space (bottom).

absorption and emission spectra synthesized by using the acquired
spectral (top left) and RGB (top right) lighting environments. Here,the spectral lighting environment is converted to the RGB lighting
environment by using the CIE color matching functions. Figure 2(bottom) shows the RMS differences between the synthesized images in the CIE-L*a*b* space. We can see both qualitatively andquantitatively that the difference between them is significant. The
reason why fluorescent materials have large differences is that theabsorption spectra are often narrow-band.
Other experimental results are shown in the supplementary material. In particular, we show that the RMS differences in the CIEL*a*b* space are less than 7 for reflective materials when the samelighting environment is used.

Acknowledgements
This work was supported by JSPS KAKENHI Grant No. 25280057.

References
BARNARD , K. 1999. Color constancy with fluorescent surfaces. In Proc.
IS&T/SID Seventh Color Imaging Conference: Color Science, Systemsand Applications, 257–261.
D EBEVEC , P. 1998. Rendering synthetic objects into real scenes: bridging
traditional and image-based graphics with global illumination and highdynamic range photography. In Proc. ACM SIGGRAPH’98, 189–198.
S ATO , I., S ATO , Y., AND I KEUCHI , K. 1999. Acquiring a radiance distribution to superimpose virtual objects onto a real scene. IEEE TVCG 5,1, 1–12.
W ILKIE , A., NAWAZ , S., D ROSKE , M., W EIDLICH , A., AND H ANIKA , J.
2014. Hero wavelength spectral sampling. Computer Graphics Forum(Proc. EGSR2014) 33, 4, 123–131.