Usability of Digital Cameras for Verifying Physically Based ...

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Figure 5: The positions of the 25 radiometric measurements [MRC + 86]. Figure 6: Results of the test scene with white panels and a white box inside it [MRC + 86]. 12
Figure 7: Points A to F were selected for measurements [TTOO90]. onments. None of the devices is able to measure specular or anisotropic surfaces. Moreover, 25 measurements are not representative for more complex scenes. But using more samples makes the procedure even more time-consuming. 2.2.2 Color Measurements In 1990, Tagaki et al. [TTOO90] wanted to verify the results of a global illumin- ation algorithm they developed to render realistic previews of car models. They reduced the comparisons to a few relevant feature points. A car was analyzed un- der different weather conditions and the most critical sections were selected (see figure 7). A Minolta CS-100 incident color meter (see appendix B.5) was used to measure the chromaticity and the luminance of these points. According to Tagaki et al., the measured and the calculated values were almost equal. Unfortunately, the veri- fication process is not described in detail. Therefore it is not clear how the measurements were acquired and why just six samples were taken. Figure 8 shows a photograph and a rendering of one of the car models. It can easily be seen that those images are not equal. A difference image (see figure 9) reveals that they are not geometrically equal and that there are big differences in color values on some parts of the car, in the shadows and in the background. Figure 10 points out the 13
Page 1 and 2: DISSERTATION Usability of Digital C
Page 3 and 4: Kurzfassung Physically Based Render
Page 5 and 6: Acknowledgements I want to thank We
Page 7 and 8: 3 Colorimetry 32 3.1 Color Spaces .
Page 9 and 10: E.5 Treating the Camera as a Black
Page 11 and 12: 20 The CIE 1931 chromaticity diagra
Page 13 and 14: 53 (a) Density plot for using the c
Page 15 and 16: List of Tables 1 Camera settings th
Page 17 and 18: 1 Introduction In recent years, a l
Page 19 and 20: 1.3 Applications of Physically Base
Page 21 and 22: matter are calculated in XYZ space.
Page 23 and 24: Figure 2: A photograph of the real
Page 25 and 26: (a) Photo (b) Default (c) 2 Bounces
Page 27: y a light source on top of the cube
Page 31 and 32: Figure 9: A difference image of pho
Page 33 and 34: Figure 12: From top to bottom: a hi
Page 35 and 36: (a) Figure 13: (a) Left side: measu
Page 37 and 38: Figure 14: Six photocells were posi
Page 39 and 40: Figure 16: The left image shows a r
Page 41 and 42: (a) (b) Figure 18: (a) The validati
Page 43 and 44: shape of the caustic. 2.3 Analytica
Page 45 and 46: two test cases, but instead of the
Page 47 and 48: segments of the sphere and its actu
Page 49 and 50: of luminance [GJB80]. In XYZ color
Page 51 and 52: where L ∗ � � Y m = 903.3 Yn
Page 53 and 54: (a) (b) Figure 21: Chromaticity dia
Page 55 and 56: 4 Digital Cameras Pattanaik et al.
Page 57 and 58: Figure 23: The RAW image data is co
Page 59 and 60: 5 Physically Based Rendering System
Page 61 and 62: see section 7.4. By avoiding XYZ sp
Page 63 and 64: Figure 27: The sensor plane marking
Page 65 and 66: (a) (b) Figure 30: (a) ColorChecker
Page 67 and 68: available on MAC systems. We chose
Page 69 and 70: (a) (b) (c) Figure 34: Standard dev
Page 71 and 72: Figure 35: Eighty measurement spots
Page 73 and 74: (a) (b) (c) (d) Figure 37: (a) Lumi
Page 75 and 76: (a) (b) Figure 40: (a) Luminance di
Page 77 and 78: i \ j 0 1 2 3 4 5 6 7 8 9 10 11 12
Page 79 and 80:
(a) (b) (c) (d) Figure 43: (a) Lumi
Page 81 and 82:
(a) (b) Figure 46: Standard deviati
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(a) (b) Figure 49: (a) The measurem
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of each patch using a spectrophotom
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Figure 51: L ∗ a ∗ b ∗ differ
Page 89 and 90:
(a) (b) Figure 53: (a) Density plot
Page 91 and 92:
(a) (b) (c) Figure 55: Histograms o
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Using an ICC profile obviously impr
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(a) (b) (c) (d) Figure 58: Histogra
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5 6 7 8 9 10 11 Min 0.50 0.49 0.13
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in the light transport simulation o
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the internal data of a camera and w
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etter results: One can measure the
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Figure 62: A Mathematica generated
Page 107 and 108:
their internal color space. This mi
Page 109 and 110:
Figure 66: Surface reflectance valu
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as bad compared to other methods. S
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in sufficient detail. The approach
Page 115 and 116:
A Radiometric and Photometric Quant
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B.6 Goniophotometer A goniophotomet
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C.3 Color Chart Figure 68: A Konica
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D Web Resources of Validation Data
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8 bit jpg 16 bit 29 11.58 11.52 11.
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8 bit jpg 16 bit 91 17.21 17.11 17.
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E.2 Using an ICC Profile 8 bit jpg
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8 bit jpg 16 bit 62 16.10 16.08 16.
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8 bit jpg 16 bit 124 11.08 10.89 11
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1 2 3 4 31 9.10 3.61 4.26 5.96 32 8
Page 135 and 136:
1 2 3 4 93 6.25 4.11 4.05 8.69 94 4
Page 137 and 138:
E.4 Adaptive Mapping - Higher Order
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5 6 7 8 9 10 11 62 3.42 2.52 2.51 2
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5 6 7 8 9 10 11 124 2.10 2.14 2.96
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R G B 31 -38.94 -25.49 29.26 32 -9.
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R G B 93 103.86 109.28 55.06 94 31.
Page 147 and 148:
References [3dS06] 3D Studio Max: w
Page 149 and 150:
[HLR00] Guowei Hong, M. Ronnier Luo
Page 151 and 152:
Color Imaging Conference, pages 250
Page 153:
[VMKK00] V. Volevich, K. Myszkowski
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