
Digitally Archiving Cultural Objects
By: Katsushi Ikeuchi (Editor), Daisuke Miyazaki (Editor)
Hardcover | 16 January 2008
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540 Pages
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A large number of cultural heritage objects around the world are deteriorating or being destroyed due to the work of natural disasters, such as earthquakes and floods, and human-inflicted destruction, such as war and vandalism. In the wake of these threats, 3D data becomes a critical component to permanently recording the shapes of these important objects so that they might be passed down to future generations.
Digitally Archiving Cultural Objects describes thorough research and methods for preserving cultural heritage objects through the use of 3D digital data. These methods were developed through using computer vision and computer graphics technologies. This data can also be used for simulation and restoration purposes as well as towards various multimedia applications.
This comprehensive book is organized into four parts. Part 1 describes various sensors designed to obtain data. Part 2 contains a collection of papers that describe the geometric pipeline, converting obtained data into a consistent geometric model, through determining relative relations among digital data and connecting those data into a uniform representation. Part 3 concerns photometric issues, including how to map color pictures on a geometric model and how to remove the effect of sunlight in the pictures obtained. Part 4 reports on the effort to establish a digital museum to restore and display the original appearance of heritage objects as well as conduct analyses of obtained data for heritage research.
| Preface | p. v |
| List of Figures | p. xvii |
| List of Tables | p. xxxiii |
| Introduction | p. 1 |
| Range Sensor | p. 2 |
| Range Data Analysis | p. 3 |
| Photometric Modelling | p. 6 |
| Utilizing Digital Data for Archaeological Investigation | p. 7 |
| Acknowledgments | p. 9 |
| References | p. 9 |
| Range Sensor | |
| Shape Rectification of 3D Data Obtained by a Moving Range Sensor by using Image Sequences | p. 13 |
| Introduction | p. 13 |
| Full Perspective Factorization | p. 15 |
| Refinement | p. 19 |
| FLRS | p. 21 |
| Experiments | p. 23 |
| Conclusions | p. 26 |
| Acknowledgments | p. 26 |
| References | p. 26 |
| The Climbing Sensor: 3D Modeling of Narrow Areas by Using Space-Time Analysis | p. 33 |
| Introduction | p. 33 |
| Spatiotemporal Range Image | p. 35 |
| Localizing Algorithm for 3D Modeling | p. 38 |
| System Configuration | p. 39 |
| Experiment and Modeling Results | p. 41 |
| Conclusion | p. 44 |
| Acknowledgments | p. 45 |
| References | p. 46 |
| Inverse Polarization Raytracing: Estimating Surface Shapes of Transparent Objects | p. 49 |
| Introduction | p. 49 |
| Polarization Raytracing | p. 51 |
| Inverse Polarization Raytracing | p. 56 |
| Measurement Result | p. 58 |
| Conclusion | p. 63 |
| Acknowledgments | p. 64 |
| References | p. 64 |
| Range Data Analysis | |
| Robust Simultaneous Registration of Multiple Range Images | p. 71 |
| Introduction | p. 71 |
| Overview | p. 72 |
| Point Mate Search | p. 75 |
| Least-square Minimization Strategy | p. 79 |
| Results | p. 82 |
| Conclusion and Future Work | p. 84 |
| Acknowledgments | p. 85 |
| References | p. 86 |
| A Fast Simultaneous Alignment of Multiple Range Images | p. 89 |
| Introduction | p. 89 |
| Alignment Algorithm | p. 91 |
| Experimental Results | p. 99 |
| Conclusion | p. 104 |
| Acknowledgments | p. 106 |
| References | p. 106 |
| Parallel Alignment of a Large Number of Range Images | p. 109 |
| Introduction | p. 109 |
| Simultaneous Alignment Algorithm | p. 110 |
| Parallel Alignment Based on a PC Cluster | p. 112 |
| Performance Evaluation | p. 118 |
| Experimental Results | p. 121 |
| Conclusion | p. 124 |
| Acknowledgments | p. 124 |
| References | p. 124 |
| Simultaneous Determination of Registration and Deformation Parameters among 3D Range Images | p. 127 |
| Introduction | p. 127 |
| Related Work | p. 129 |
| Robust Determination of Translation and Rotation Parameters | p. 130 |
| Simultaneous Determination of Deformation Parameters | p. 132 |
| Unknown Parameter Estimation of Mathematical Model | p. 134 |
| Inter-and-Intra Scanning Registration | p. 138 |
| Conclusion and Future Work | p. 143 |
| Acknowledgments | p. 144 |
| References | p. 144 |
| Parallel Processing of Range Data Merging | p. 147 |
| Introduction | p. 147 |
| Data Merging | p. 148 |
| Increase the computation and memory efficiency | p. 152 |
| Parallel Computing of Signed Distances | p. 153 |
| Performance Evaluation | p. 155 |
| Conclusion | p. 157 |
| Acknowledgments | p. 157 |
| References | p. 158 |
| Adaptively Merging Large-Scale Range Data with Reflectance Properties | p. 161 |
| Introduction | p. 161 |
| Consensus Surface Algorithm | p. 166 |
| Adaptive Merging Algorithm | p. 168 |
| Adaptive Merging with Reflectance Properties | p. 173 |
| Adaptive Nearest Neighbor Search | p. 177 |
| Experiments | p. 179 |
| Conclusion | p. 183 |
| Appendix: Algorithm of BOT test | p. 186 |
| Acknowledgments | p. 187 |
| References | p. 187 |
| Iterative Refinement of Range Images with Anisotropic Error Distribution | p. 193 |
| Introduction | p. 193 |
| Proposed Method | p. 194 |
| Experiment | p. 198 |
| Summary | p. 203 |
| Acknowledgments | p. 204 |
| References | p. 205 |
| Hole Filling of 3D Model by Flipping Signs of Signed Distance Field in Adaptive Resolution | p. 207 |
| Introduction | p. 207 |
| Hole Filling by Minimizing the Curvature of the Interpolating Surface | p. 209 |
| Computing Initial State from Real Surfaces | p. 210 |
| Hole Filling by Minimizing the Area of Interpolating Surface | p. 214 |
| Experiments | p. 223 |
| Conclusion | p. 231 |
| Acknowledgments | p. 232 |
| References | p. 232 |
| Color Analysis | |
| Simultaneous Registration of 2D Images onto 3D Models for Texture Mapping | p. 237 |
| Introduction | p. 237 |
| Preliminaries | p. 242 |
| 2D-3D Registration Algorithm | p. 246 |
| Simultaneous Registration Algorithm | p. 258 |
| Experiments and Results | p. 264 |
| Conclusions | p. 267 |
| Acknowledgments | p. 268 |
| References | p. 268 |
| Consistent Surface Color for Texturing Large Objects in Outdoor Scenes | p. 279 |
| Introduction | p. 280 |
| Reflection Model | p. 281 |
| Estimating Surface Chromaticity | p. 282 |
| Robust Framework for Outdoor Scenes | p. 285 |
| Implementation and Experimental Results | p. 289 |
| Conclusion | p. 291 |
| Appendix | p. 293 |
| Appendix 14.B | p. 294 |
| Acknowledgments | p. 294 |
| References | p. 294 |
| Separating Illumination and Surface Spectral from Multiple Color Signals | p. 297 |
| Introduction | p. 297 |
| Theoretical Background | p. 299 |
| Proposed Method: Separation using Multiple Color Signals | p. 301 |
| Acquiring Scene Spectrums | p. 307 |
| Implementation | p. 310 |
| Experimental Results | p. 311 |
| Conclusion | p. 319 |
| Acknowledgments | p. 319 |
| References | p. 319 |
| Color Constancy through Inverse-Intensity Chromaticity Space | p. 323 |
| Introduction | p. 323 |
| Reflection Model | p. 326 |
| Inverse-Intensity Chromaticity Space | p. 328 |
| Computational Method to Estimate Illumination Chromaticity | p. 333 |
| Discussion | p. 334 |
| Experimental Results | p. 338 |
| Conclusion | p. 348 |
| Acknowledgments | p. 349 |
| References | p. 349 |
| Separating Reflection Components of Textured Surfaces using a Single Image | p. 353 |
| Introduction | p. 353 |
| Reflection Model | p. 358 |
| Specular-to-diffuse mechanism | p. 360 |
| Separation Method | p. 366 |
| Implementation | p. 370 |
| Discussion | p. 372 |
| Experimental Results | p. 374 |
| Conclusion | p. 375 |
| p. 378 | |
| p. 378 | |
| Acknowledgments | p. 381 |
| References | p. 382 |
| Creating Photorealistic Virtual Model with Polarization-based Vision System | p. 385 |
| Introduction | p. 385 |
| Reflection Mechanism | p. 388 |
| Polarization | p. 392 |
| Data Acquisition System | p. 394 |
| Separation of Reflection Components | p. 395 |
| Parameter Estimation | p. 397 |
| Synthesized Images | p. 398 |
| Conclusion | p. 398 |
| Acknowledgments | p. 399 |
| References | p. 399 |
| Digitizing Cultural Heritage | |
| Classification of Bayon Faces | p. 405 |
| Introduction | p. 405 |
| Bayon face | p. 406 |
| Classification technique | p. 408 |
| Experiments | p. 413 |
| Conclusion | p. 417 |
| Acknowledgments | p. 417 |
| References | p. 417 |
| Illumination Simulation for Archaeological Investigation | p. 419 |
| Introduction | p. 419 |
| Related Work | p. 420 |
| Acquisition of Geometric Information | p. 421 |
| Acquisition of Photometric Information | p. 422 |
| Fugoppe Cave | p. 422 |
| Ozuka Tumulus | p. 427 |
| Summary | p. 430 |
| Acknowledgments | p. 430 |
| References | p. 430 |
| Editing, Retrieval, and Display System of Archeological Information on Large 3D Geometric Models | p. 441 |
| Introduction | p. 442 |
| Overview of Proposed System | p. 443 |
| Browsing | p. 444 |
| Associating Information | p. 446 |
| Experimental Result | p. 450 |
| Conclusion | p. 453 |
| Acknowledgments | p. 453 |
| References | p. 454 |
| Virtual Asukakyo: Real-time Soft Shadows in Mixed Reality using Shadowing Planes | p. 457 |
| Introduction | p. 457 |
| Generation of Shadowing Planes and Basis Images | p. 458 |
| Real-Time Shadowing Process | p. 463 |
| Experimental Result | p. 464 |
| Virtual Asukakyo Project | p. 466 |
| Conclusion | p. 469 |
| References | p. 470 |
| Digital Restoration of the Nara Great Buddha | p. 473 |
| Introduction | p. 473 |
| Toudaiji and the Great Buddha | p. 474 |
| Restoring the Buddha Statue | p. 475 |
| Restoring the Buddha Palace | p. 476 |
| Analysis | p. 477 |
| Conclusion | p. 481 |
| Acknowledgments | p. 483 |
| References | p. 483 |
| Gallery | |
| References | p. 497 |
| Index | p. 501 |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9780387758060
ISBN-10: 0387758062
Published: 16th January 2008
Format: Hardcover
Language: English
Number of Pages: 540
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: US
Dimensions (cm): 23.5 x 16.51 x 3.18
Weight (kg): 0.84
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