Usability of Digital Cameras for Verifying Physically Based ...

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17 L ∗ a ∗ b ∗ differences for all 140 patches of the three test cases for using an ICC profile. . . . . . . . . . . . . . . . . . . . . . . . . 115 18 L ∗ a ∗ b ∗ differences for all 140 patches for linear polynomials when using the adaptive mapping. . . . . . . . . . . . . . . . . . . . . 120 19 L ∗ a ∗ b ∗ differences for all 140 patches for higher order polynomials when using the adaptive mapping. . . . . . . . . . . . . . . . 125 20 Residuals for all 140 patches for channels R, G and B when treating the camera as a black box. . . . . . . . . . . . . . . . . . . . 130 xv
1 Introduction In recent years, a lot of work has been published in the field of photorealistic computer graphics concerning more accurate rendering algorithms, more detailed descriptions of surfaces, more realistic tone mapping algorithms and many other improvements for the process of rendering photorealistic images. Unfortunately, there have been comparatively few attempts on verifying all these algorithms in practice. In the field of photorealistic rendering, it should be common practice to compare a rendered image to measurements obtained from a real-world scene. This is currently the only practicable way to prove the correctness of the im- plementation of a true global illumination algorithm. However, most rendering algorithms are just verified by visual inspection of their results. The process of verifying a photorealistic renderer can be divided into three steps [GTS + 97]. First, one has to prove the correctness of the light reflection models through comparisons with measured physical experiments. One way to do this is to use a gonioreflectometer to measure the full bidirectional reflectance dis- tribution functions (BRDFs) of all surfaces. The second step is to verify the light transport simulation, or – in other words – the actual rendering algorithm. This can be done by comparing the rendered image to a measurement of a real scene, or through analytic approaches. The final step in image synthesis generates an image from radiometric data provided by the rendering algorithm; it has to take the properties of the output device and the actual viewing conditions into account. In this stage psychophysical models are used to achieve convincing results, therefore perceptual experiments are used to evaluate how believable such an image is. This thesis will focus on the second step: the verification of rendering algorithms. Although this step is crucial in photorealistic image synthesis, comparatively little work has been published about this topic so far. The problem is far from being solved, though. Most of the available rendering systems are not verified. 1.1 Motivation The goal of photorealistic rendering is to generate images that look like photo- graphs of a real scene. To verify such a rendering system, the obvious way is to compare a photograph to a synthetic image of the same scene. This thesis fo- 1
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: List of Tables 1 Camera settings th
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 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
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available on MAC systems. We chose
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(a) (b) (c) Figure 34: Standard dev
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Figure 35: Eighty measurement spots
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(a) (b) (c) (d) Figure 37: (a) Lumi
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(a) (b) Figure 40: (a) Luminance di
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i \ j 0 1 2 3 4 5 6 7 8 9 10 11 12
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(a) (b) (c) (d) Figure 43: (a) Lumi
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(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
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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
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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
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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
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1 2 3 4 93 6.25 4.11 4.05 8.69 94 4
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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.
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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
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[VMKK00] V. Volevich, K. Myszkowski
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