Usability of Digital Cameras for Verifying Physically Based ...

Figure 17: Illuminance values measured with a luxmeter at sampled points on the
floor of the atrium [DM01].
well. For a high quality rendering, the average simulation error was 10.5%.
Moreover, Drago and Myszkowski did a visual comparison of a rendered im-
age, a photograph, the real scene and a tuned rendering, i.e. the Lambertian and
mirror reflection coefficients were intuitively tuned by a skilled artist to get the
best match of image appearance in respect to the real scene. They asked 25 sub-
jects to rate how similar the images were in respect to the real atrium scene. In
all cases, the photograph got the highest score. The tuned image was found to
be more realistic than the rendered image in terms of overall lighting and tone,
which is not surprising because it was post-processed. This might be part of the
explanation why industry prefers tweaking rendering parameters instead of doing
physically based renderings. But it has to be mentioned that the artistic approach
cannot be used if the scene does not exist in reality, i.e. when predictions of a
scene have to be generated.
2.2.9 Component Case Studies
Recently, Schregle and Wienold [SW04] presented another approach for using
luxmeters to verify a photorealistic rendering system, which is also the topic of
Schregle’s PhD thesis. Unlike Drago and Myszkowski, they focused on a setup
that can be used to test each effect of global illumination separately. Therefore,
they built a box similar to the Cornell box (see figure 18(a)).
On the top, the bottom, and the sides of the box, belts were mounted in or-
der to guide the sensors. The belts were covered with the interior material of
the box. They were driven in parallel by a shaft, which was operated manually.
Figure 18(b) shows a schematic of the sensor guidance mechanism.
Moreover, there were four additional sensors mounted on the front face of the
box to measure the direct illuminance from the light source. So, the authors were
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