Modeling from Reality

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This book summarizes the results of our modeling-from-reality (MFR) project which took place over the last decade or so. The goal of this project is to develop techniques for modeling real objects and/or environments into geometric and photometric models through computer vision techniques. By developing such techniques, time consuming modeling process, currently un- dertaken by human programmers, can be (semi-)automatically performed, and, as a result, we can drastically shorten the developing time of such virtual reality systems, reduce their developing cost, and widen their application areas. Originally, we began to develop geometric modeling techniques that acquire shape information of objects/environments for object recognition. Soon, this effort evolved into an independent modeling project, virtual-reality modeling, with the inclusion of photometric modeling aspects that acquire appearance information, such as color, texture, and smoothness. Over the course of this development, it became apparent that environmental modeling techniques were necessary when applying our techniques to mixed realities that seamlessly combine generated virtual models with other real/virtual images. The material in his book covers these aspects of development.
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Product details

  • Hardback | 199 pages
  • 157.5 x 238.8 x 17.8mm | 498.96g
  • Dordrecht, Netherlands
  • English
  • 2001 ed.
  • XXIX, 199 p.
  • 0792375157
  • 9780792375159

Table of contents

List of Figures. Preface. Introduction; K. Ikeuchi. Part I: Geometric Modeling. 1. Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling; H. Shum, K. Ikeuchi, R. Reddy. 1. Introduction. 2. Principal Component Analysis with Missing Data. 3. Merging Multiple Views. 4. Surface Patch Tracking. 5. Spatial Connectivity. 6. Experiments. 7. Concluding Remarks.2. Building 3-D Models from Unregistered Range Images; K. Higuchi, M. Hebert, K. Ikeuchi 1. Introduction. 2. Spherical Attribute Images. 3. Registering Multiple Views. 4. Building a Complete Model. 5. Conclusion. 3. Consensus Surfaces for Modeling 3DObjects from Multiple Range Images; M.D. Wheeler, Y. Sato, K. Ikeuchi. 1. Introduction. 2. Approach. 3. Data Merging. 4. Experimental Results. 5. Conclusion. Part II: Photometric Modeling. 4. Object Shape and Reflectance Modeling from Observation; Y. Sato, M.D. Wheeler, K. Ikeuchi. 1. Introduction. 2. Image Acquisition System. 3. Surface Shape Modeling. 4. Surface Reflectance Modeling. 5. Image Synthesis. 6. Conclusion. 5. Eigen-Texture Method : Appearance Compression based on 3D Model; K. Nishino, Y. Sato, K. Ikeuchi.1. Introduction.2. Eigen-Texture Method.3. Implementation. 4. Integrating into real scene. 5. Conclusions. Part III: Environmental Modeling. 6. Acquiring a Radiance Distribution to Superimpose Virtual Objects onto a Real Scene; I. Sato, Y. Sato, K. Ikeuchi. 1. Introduction. 2. Consistency of Geometry. 3. Consistency of Illumination. 4. Superimposing Virtual Objects onto a Real Scene. 5. Experimental Results. 6. Conclusions. 7. Illumination Distribution from Shadows; I. Sato, Y. Sato, K. Ikeuchi. 1. Introduction. 2. Formula for RelatingIllumination Radiance with Image Irradiance. 3. Estimation of Illumination Distribution Using Image Irradiance. 4. Experimental Results. 5. Conclusions. Part IV: Epilogue: MFR to Digitized Great Buddha. 8. The Great Buddha Project: Modeling Cultural Heritage through Observation; D. Miyazaki, T. Oishi, T. Nishikawa, R. Sagawa, K. Nishino, T. Tomomatsu, Y. Takase, K. Ikeuchi. 1. Introduction. 2. Modeling from Reality. 3. Modeling the Great Buddha of Kamakura. References. Index.
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