Usability of Digital Cameras for Verifying Physically Based ...

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2 Related Work 2.1 Visual Comparisons A practicable way of verifying a renderer is to compare the results of the rendering process to a real scene. The very first approach in this direction was done in 1975 by Phong [Pho75]. He compared a phong-shaded sphere to a photograph of a real sphere. The two images differ apparently but he did not discuss this fact any further. As the Phong model is not a physically based global illumination algorithm, we will not go into detail. Two more representative approaches are discussed in the following sections. 2.1.1 The Original Cornell Box Figure 1: A schematic of the first Cornell Box setup [GTGB84]. The first approach in this direction was done in 1984 by Goral et al. [GTGB84]. They showed that the radiosity algorithm behaves similar to light propagation in a real setup of the scene. One reason for choosing this method was that the rendering 6
Figure 2: A photograph of the real cube [GTGB84]. equation had not been defined in the form known today [Kaj86]. The setup – also known as the Cornell Box – consisted of a cube made out of fiber board panels, colored with flat latex paints to minimize specular reflections. The panels were painted with different colors to evoke the effect of color-bleeding. The cube had just five sides to be able to take photographs of the scene and to illuminate the inside with diffuse white light. A schematic of the setup can be seen in figure 1. In order to simulate a diffuse and uniform area light source at the front side of the cube, the point light sources did not face the cube but a diffuse white surface. In front of the cube, there was another white surface that contained a small hole for the camera. Due to the multiple reflections, the lighting can therefore be considered diffuse. Due to the lack of color measurement devices, no quantitative but only visual comparisons could be made. Hence, the result of the comparison is very superfi- cial. It can only be said that the color-bleeding that is visible on a photograph of the cube (see figure 2) is also present on a rendered image of the scene (see figure 3). So, the structure of the light distribution can be verified but not the colors. They do not correspond to each other, e.g. the white surfaces of the photograph look much redder than the ones of the simulation. This is probably caused by using light bulbs that emit reddish light. 7
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: matter are calculated in XYZ space.
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
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
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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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