Usability of Digital Cameras for Verifying Physically Based ...

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Contents 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Believable vs. Correct . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Applications of Physically Based Rendering . . . . . . . . . . . . 3 1.3.1 Virtual Prototyping . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Architecture and Lighting Design . . . . . . . . . . . . . 3 1.3.3 Medical Reconstruction . . . . . . . . . . . . . . . . . . 3 1.3.4 Safety Checks . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work 6 2.1 Visual Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 The Original Cornell Box . . . . . . . . . . . . . . . . . 6 2.1.2 Recent developments . . . . . . . . . . . . . . . . . . . . 8 2.2 Experimental Measurements . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Improved Cornell Box . . . . . . . . . . . . . . . . . . . 10 2.2.2 Color Measurements . . . . . . . . . . . . . . . . . . . . 13 2.2.3 Scanning the Cornell Box . . . . . . . . . . . . . . . . . 14 2.2.4 Model of an Office Room . . . . . . . . . . . . . . . . . 16 2.2.5 Charge-coupled Device (CCD) . . . . . . . . . . . . . . . 18 2.2.6 Evaluation of Four Daylighting Software Programs . . . . 20 2.2.7 Validation of Daylight Simulations using Radiance . . . . 20 2.2.8 Model of the Atrium at the University of Aizu . . . . . . . 22 2.2.9 Component Case Studies . . . . . . . . . . . . . . . . . . 24 2.3 Analytical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.1 Scenes With a Constant Solution . . . . . . . . . . . . . . 27 2.3.2 A Representative Set of Test Cases . . . . . . . . . . . . . 28 2.4 Combined Approaches . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.1 Comparison of several Rendering Systems . . . . . . . . 29 2.4.2 A Multistage Validation Procedure . . . . . . . . . . . . . 30 v
3 Colorimetry 32 3.1 Color Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.1.1 CIE XYZ Color Space . . . . . . . . . . . . . . . . . . . 32 3.1.2 CIE L ∗ a ∗ b ∗ Color Space . . . . . . . . . . . . . . . . . . 33 3.1.3 CIE L ∗ u ∗ v ∗ Color Space . . . . . . . . . . . . . . . . . . 35 3.1.4 RGB Color Spaces . . . . . . . . . . . . . . . . . . . . . 37 3.2 Color Difference Formulae . . . . . . . . . . . . . . . . . . . . . 38 4 Digital Cameras 39 4.1 Various Forms of Output . . . . . . . . . . . . . . . . . . . . . . 39 5 Physically Based Rendering Systems 43 6 Data Acquisition and Processing 46 6.1 Photographs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 6.1.1 Position and Orientation of the Camera . . . . . . . . . . 46 6.1.2 Representative RGB Values . . . . . . . . . . . . . . . . 47 6.1.3 Image Averaging . . . . . . . . . . . . . . . . . . . . . . 51 6.1.4 RAW image . . . . . . . . . . . . . . . . . . . . . . . . . 54 6.2 Light Cabinet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 6.3 Spectral Measurements . . . . . . . . . . . . . . . . . . . . . . . 68 7 Various Approaches of Using a Digital Camera for Verification Pur- poses 70 7.1 Using the Camera’s Color Space . . . . . . . . . . . . . . . . . . 70 7.2 Using an ICC Profile . . . . . . . . . . . . . . . . . . . . . . . . 73 7.3 Implementing an Adapted Mapping . . . . . . . . . . . . . . . . 77 7.4 Treating the Camera as a Black Box . . . . . . . . . . . . . . . . 82 7.5 Other Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.5.1 Imitation of a Multi-spectral Camera . . . . . . . . . . . . 85 7.5.2 Converting RGB Values to Spectra . . . . . . . . . . . . . 85 7.5.3 Simulating the CCD Sensor . . . . . . . . . . . . . . . . 86 8 A Problem with the Use of XYZ Color Space for Photorealistic Ren- dering Computations 88 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 vi
Page 1 and 2: DISSERTATION Usability of Digital C
Page 3 and 4: Kurzfassung Physically Based Render
Page 5: Acknowledgements I want to thank We
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 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
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see section 7.4. By avoiding XYZ sp
Page 63 and 64:
Figure 27: The sensor plane marking
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(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
Page 73 and 74:
(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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