Usability of Digital Cameras for Verifying Physically Based ...

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Figure 64: A Mathematica generated plot of the domain of valid XYZ values with the larger surface of possible result values from component-wise multiplication superimposed over it. Figure 65: A visualization of both the gamut of valid XYZ values (in false-color) and the gamut of triplets that can be generated by component-wise multiplication of these valid XYZ values (gray). The slight overlapping on the edges occurs because of different clipping planes (produced by Andrea Weidlich). 92
Figure 66: Surface reflectance values form a comparatively small subset (gray volume) of all possible XYZ colors (produced by Andrea Weidlich). ues which describe a light – which can be arbitrarily saturated – and surface reflectance values, which form a comparatively small subset of all possible XYZ colors [Wys62]; see figure 66 for a qualitative sketch. While the shape of the subset is not correctly represented in this plot it still shows its key feature as far as the topic of this paper is concerned: even its most saturated colors are located at comparatively large distances from the gamut boundary, which means that com- binations of surface colors and light values are very unlikely to produce invalid results. Since the interactions which potentially produce invalid results are those where both operands are highly saturated colors (i.e. those which are already near the boundary of the solid of valid XYZ colors) this means that realistic light and surface interactions are not likely to produce the kind of problem we are trying to illustrate here. However, the theoretical weakness of XYZ space as a color space for rendering calculations remains, especially since some kinds of light and matter interactions (such as calculations involving transparency, diffraction effects or disper- sion) can lead to situations where invalid colors are easily produced. 93
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DISSERTATION Usability of Digital C
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Kurzfassung Physically Based Render
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Acknowledgements I want to thank We
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3 Colorimetry 32 3.1 Color Spaces .
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E.5 Treating the Camera as a Black
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20 The CIE 1931 chromaticity diagra
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53 (a) Density plot for using the c
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List of Tables 1 Camera settings th
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1 Introduction In recent years, a l
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1.3 Applications of Physically Base
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matter are calculated in XYZ space.
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Figure 2: A photograph of the real
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(a) Photo (b) Default (c) 2 Bounces
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y a light source on top of the cube
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Figure 7: Points A to F were select
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Figure 9: A difference image of pho
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Figure 12: From top to bottom: a hi
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(a) Figure 13: (a) Left side: measu
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Figure 14: Six photocells were posi
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Figure 16: The left image shows a r
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(a) (b) Figure 18: (a) The validati
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shape of the caustic. 2.3 Analytica
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two test cases, but instead of the
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segments of the sphere and its actu
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of luminance [GJB80]. In XYZ color
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where L ∗ � � Y m = 903.3 Yn
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(a) (b) Figure 21: Chromaticity dia
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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
Page 85 and 86: of each patch using a spectrophotom
Page 87 and 88: 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
Page 93 and 94: Using an ICC profile obviously impr
Page 95 and 96: (a) (b) (c) (d) Figure 58: Histogra
Page 97 and 98: 5 6 7 8 9 10 11 Min 0.50 0.49 0.13
Page 99 and 100: in the light transport simulation o
Page 101 and 102: the internal data of a camera and w
Page 103 and 104: etter results: One can measure the
Page 105 and 106: Figure 62: A Mathematica generated
Page 107: their internal color space. This mi
Page 111 and 112: as bad compared to other methods. S
Page 113 and 114: in sufficient detail. The approach
Page 115 and 116: A Radiometric and Photometric Quant
Page 117 and 118: B.6 Goniophotometer A goniophotomet
Page 119 and 120: C.3 Color Chart Figure 68: A Konica
Page 121 and 122: D Web Resources of Validation Data
Page 123 and 124: 8 bit jpg 16 bit 29 11.58 11.52 11.
Page 127 and 128: E.2 Using an ICC Profile 8 bit jpg
Page 131 and 132: 8 bit jpg 16 bit 124 11.08 10.89 11
Page 133 and 134: 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
Page 139 and 140: 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
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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