Usability of Digital Cameras for Verifying Physically Based ...

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In the second approach, the authors compared whole surface patches instead of points. They did not increase the number of measurements but took a photograph of the scene and scanned it for further processing. Hence the luminance values are known for some of the pixels of the photograph, the other values could be calculated by using a curve fitting function. The root mean square error was now between 16.4% and 18.5% and the average relative error was between 44% and 71%. Most of the errors occurred because of a misalignment of the images. Moreover it should be mentioned, that for one of the scenes the root mean square error for the low quality rendering was again lower than for the one with high quality. In order to cope with the problem of misalignment, the edges were excluded from the evaluation for testing purposes. This reduced the root mean square error to a value between 12.9% and 17.8% and the average relative error to a value between 32% and 52%. However, the margin of error was still very high. One reason for this was that the specifications of the light sources differed by up to 30% from the true value. Furthermore, the devices used to measure the material characteristics introduced an error in the range of 5% to 10%. 2.2.5 Charge-coupled Device (CCD) In 1997, the Cornell Box approach was again used for verification purposes. Now a CCD camera was used for direct colorimetric comparisons of synthetic and real images. This was the first attempt where values for the whole image and not just a few samples were captured. Pattanaik et al. [PFTG97] describe the procedure of calibrating a CCD camera to reduce the error that is introduced by different forms of noise and the extraction of color values out of a monochromatic CCD. Seven narrow band filters were used to distinguish between the different ranges of wavelengths. Then, the CIE tristimulus values (X,Y ,Z) were computed for each CCD element and for each pixel of the computer generated image. Fig- ure 13(a) shows the results of those calculations. A difference in color values is clearly visible. The measured image is redder in the upper left corner whereas the computed image appears greener on the right side. A scaled difference image of the luminance values Y can be seen in figure 13(b). It can be seen that the largest errors occur at the edges of the objects and on the light source. 18
(a) Figure 13: (a) Left side: measured image. Right side: computed image; (b) scaled difference image of the luminance values of the images shown in (a) [PFTG97]. (b) 19
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 and 28: y a light source on top of the cube
Page 29 and 30: Figure 7: Points A to F were select
Page 31 and 32: Figure 9: A difference image of pho
Page 33: Figure 12: From top to bottom: a hi
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
Page 83 and 84: (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
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(a) (b) Figure 53: (a) Density plot
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(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
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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
Page 141 and 142:
5 6 7 8 9 10 11 124 2.10 2.14 2.96
Page 143 and 144:
R G B 31 -38.94 -25.49 29.26 32 -9.
Page 145 and 146:
R G B 93 103.86 109.28 55.06 94 31.
Page 147 and 148:
References [3dS06] 3D Studio Max: w
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[HLR00] Guowei Hong, M. Ronnier Luo
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Color Imaging Conference, pages 250
Page 153:
[VMKK00] V. Volevich, K. Myszkowski
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