PhD thesis - Institute for Computer Graphics and Vision - Graz ...

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vi but not least Roli, whose legendary parties will be remembered for a long, long time. Additionally, I had fruitful and interesting discussions with Peter, Matthias, Suri, Markus, Alex and especially with Martin, who shared the office with me now for so many years. Finishing this thesis was not possible without some additional activities freeing the mind and relaxing the body. At first I would like to thank all Aikido teachers and fellows on the tatami from Graz, who worked hard for the last seven years to make my body less stiff. Furthermore, I would like to thank Vera for persuading me to start dancing lessons with her. She is not only a clever and ambitious person, but she was additionally discovered as a gifted partner in the dance hall. Graz, January 2007 Christopher Zach The problem is not that people will steal your ideas. On the contrary, your job as an academic is to ensure that they do. Tom’s advice, according to Frank Dellaert
Contents 1 Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Using Graphics Processing Units for Computer Vision . . . . . . . . . . . . 2 1.3 3D Models from Multiple Images . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Overview of this Thesis and Contributions . . . . . . . . . . . . . . . . . . . 10 2 Related Work 15 2.1 Dense Depth and Model Estimation . . . . . . . . . . . . . . . . . . . . . . 15 2.1.1 Computational Stereo on Rectified Images . . . . . . . . . . . . . . . 15 2.1.2 Multi-View Depth Estimation . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Direct 3D Model Reconstruction . . . . . . . . . . . . . . . . . . . . 18 2.2 GPU-based 3D Model Computation . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 General Purpose Computations on the GPU . . . . . . . . . . . . . 19 2.2.2 Real-time and GPU-Accelerated Dense Reconstruction from Multiple Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3 Mesh-based Stereo Reconstruction Using Graphics Hardware 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Overview of Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.1 Image Warping and Difference Image Computation . . . . . . . . . . 29 3.2.2 Local Error Summation . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.3 Determining the Best Local Modification . . . . . . . . . . . . . . . 31 3.2.4 Hierarchical Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 Mesh Rendering and Image Warping . . . . . . . . . . . . . . . . . . 33 3.3.2 Local Error Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 Encoding of Integers in RGB Channels . . . . . . . . . . . . . . . . . 35 3.4 Performance Enhancements . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.1 Amortized Difference Image Generation . . . . . . . . . . . . . . . . 36 3.4.2 Parallel Image Transforms . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.3 Minimum Determination Using the Depth Test . . . . . . . . . . . . 37 vii
Page 1: Graz University of Technology Insti
Page 5: Acknowledgments Writing a PhD thesi
Page 9 and 10: CONTENTS ix 6.2.2 Regularization .
Page 11 and 12: List of Figures 1.1 Several reconst
Page 13: LIST OF FIGURES xiii 9.3 Fused 3D m
Page 17 and 18: Chapter 1 Introduction Contents 1.1
Page 19 and 20: 1.2. Using Graphics Processing Unit
Page 21 and 22: 1.3. 3D Models from Multiple Images
Page 27 and 28: 1.4. Overview of this Thesis and Co
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Page 32 and 33: 16 Chapter 2. Related Work given in
Page 34 and 35: 18 Chapter 2. Related Work lacking
Page 36 and 37: 20 Chapter 2. Related Work to semin
Page 38 and 39: 22 Chapter 2. Related Work algorith
Page 40 and 41: 24 Chapter 2. Related Work Typicall
Page 43 and 44: Chapter 3 Mesh-based Stereo Reconst
Page 45 and 46: 3.2. Overview of Our Method 29 Mesh
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Page 49 and 50: 3.3. Implementation 33 3.3 Implemen
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Page 55 and 56: 3.5. Results 39 PARAM depth_index =
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3.6. Discussion 41 (a) The key imag
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Chapter 4 GPU-based Depth Map Estim
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4.2. Plane Sweep Depth Estimation 4
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4.3. Sparse Belief Propagation 51 i
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4.3. Sparse Belief Propagation 53 p
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4.5. Timing Results 55 spaced in a
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4.5. Timing Results 57 passes to ca
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4.7. Discussion 59 maps, is still u
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4.7. Discussion 61 (a) Without BP (
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Chapter 5 Space Carving on 3D Graph
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5.2. Volumetric Scene Reconstructio
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5.3. Single Sweep Voxel Coloring in
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5.3. Single Sweep Voxel Coloring in
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5.4. Extensions to Multi Sweep Spac
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5.6. Discussion 73 prior knowledge
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5.6. Discussion 75 (a) (b) (c) (d)
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5.6. Discussion 77 (a) (b) (c) (d)
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Chapter 6 PDE-based Depth Estimatio
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6.2. Variational Techniques for Mul
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6.2. Variational Techniques for Mul
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6.3. GPU-based Implementation 85 6.
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6.3. GPU-based Implementation 87 6.
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6.4. Results 89 Figure 6.2: A recon
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6.4. Results 91 (a) Without back-ma
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6.5. Discussion 93 Nevertheless, th
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6.5. Discussion 95 (a) (b) Figure 6
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Chapter 7 Scanline Optimization for
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7.2. Scanline Optimization on the G
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7.3. Cross-Correlation based Multiv
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7.4. Discussion 115 this method uti
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7.4. Discussion 117 (a) left view (
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120 Chapter 8. Volumetric 3D Model
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Chapter 9 Results Contents 9.1 Intr
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9.2. Synthetic Sphere Dataset 133 (
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9.3. Synthetic House Dataset 135 to
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9.4. Middlebury Multi-View Stereo T
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9.5. Statue of Emperor Charles VI 1
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9.6. Bodhisattva Figure 141 (a) Fro
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9.6. Bodhisattva Figure 143 (a) Fro
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9.6. Bodhisattva Figure 145 (a) (b)
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148 Chapter 10. Concluding Remarks
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Appendix A Selected Publications A.
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Bibliography [Akbarzadeh et al., 20
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BIBLIOGRAPHY 155 [Curless and Levoy
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BIBLIOGRAPHY 157 [Grabner, 2003] Gr
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BIBLIOGRAPHY 159 [Klaus et al., 200
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BIBLIOGRAPHY 161 [Mairal and Kerive
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BIBLIOGRAPHY 163 [Pollefeys et al.,
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BIBLIOGRAPHY 165 [Strecha and Van G
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BIBLIOGRAPHY 167 [Yang et al., 2006
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PhD thesis - Institute for Computer Graphics and Vision - Graz ...

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