Usability of Digital Cameras for Verifying Physically Based ...

More documents

Recommendations

Info

Secondly, one of the LVS example scenes was rendered with each of the systems, and the results were compared to each other at three sample points. Kho- dulev and Kopylov found out that two of the systems (Specter and Radiance) yielded quite similar results whereas the result of the third system (LVS) was significantly different from the two others. They deduced that the first two systems were more accurate than the third one. As no reference measurements or analytical results of this scene were available, the validity of this comparison is questionable. Thirdly, they did a visual comparison of various complex test scenes. Al- though visual comparisons are not very accurate, some basic knowledge can nev- ertheless be gathered. For example, the specular reflections in the test images were considerably different. Furthermore, it could be seen whether a system had problems with e.g. very thin triangles or special surfaces. With visual comparisons it is possible to discover noticable weaknesses of the systems, but not to determine the physical accuracy of the resulting images. The result of this comparison was that concerning physical accuracy Radiance performed best, followed by Specter and LVS. The use of different verification methods enhanced the quality of this study. The physical accuracy of the systems was evaluated using analytical tests while more general problems were detected by visually comparing the calculated images. 2.4.2 A Multistage Validation Procedure Myszkowski and Kunii [MK00] defined a multistage validation procedure in order to validate an enhanced radiosity algorithm. It consisted of three stages: analytical tests, comparison with measurements results and visual comparisons. The authors used two different test scenes for analytical comparisons: the interior of a diffuse empty cube and the interior of a diffuse empty sphere with one or two orthogonal mirrors inside. The total lighting in a scene can be divided into the direct and the indirect illumination. For direct illumination, the theoretically derived result and the simulation matched exactly. For indirect illumination, the simulated results diverged slightly from the correct solution. This is because the energy transfer for the chosen sample points is either over- or underestimated due to approximations introduced into the form factor estimates. An additional source of error was the triangulation of the sphere, which led to differences between the 30
segments of the sphere and its actual radius. For experimental measurements, the authors again created two different test scenes. Each of the scenes consisted of an empty room with four light sources mounted on stands and directed toward the ceiling. The only difference was the height of the rooms. Unfortunately, the authors do not describe the measurement procedure in detail, as for example what kind of measurement device they used. Since what really matters in practice is how a rendering is perceived by a human observer, the synthetic images were also compared to photographs of a real scene. Therefore, the authors did a case study where they chose an atrium of the University of Aizu as reference scene. In section 2.2.8 this case study is described in more detail. By combining three validation approaches and doing such exhaustive comparisons, the authors wanted to compensate for the disadvantages of each approach. Furthermore they wanted to make the global illumination community aware of the fact that there is no standardized approach available, although so many different light transport simulation algorithms have been developed in the last decades. As their method of combining different approaches covers diverse aspects of validation it could serve as a basis for the development of a standardized validation method. 31
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 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: two test cases, but instead of the
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
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 and 108:
their internal color space. This mi
Page 109 and 110:
Figure 66: Surface reflectance valu
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 125 and 126:
8 bit jpg 16 bit 91 17.21 17.11 17.
Page 127 and 128:
E.2 Using an ICC Profile 8 bit jpg
Page 129 and 130:
8 bit jpg 16 bit 62 16.10 16.08 16.
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
show all

Usability of Digital Cameras for Verifying Physically Based ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?