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Photometric Object Modeling –rendering from a Dense/sparse Set of Images– Image-based Rendering, Inverse Rendering, 3d Photography 2 Eigen-texture Rendering Rendering from a Sparse Set of Images
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nishino, Ko Redmond Sinclair, Mike Liu, Zicheng Shan, Ying Paul, George V. Kawamura, K. Nishikawa, T. |
| Abstract | For some time, rendering photorealistic synthetic images from observations of real objects has been a major research topic in the computer vision and computer graphics communities. An extensive amount of work in this research area has lead to a few representative schools: image-based rendering, inverse rendering and 3D photography. However, each of these methods still suffers from several drawbacks, such as massive data storage, constraints on applicable objects , restrictive scenarios on application, etc. To overcome these problems, we propose two methods. The first method, Eigen-Texture Rendering, handles the appearance variation of the target object on its own 2D surface, enabling effective compression and interpolation with PCA thereby resulting in a compact representation of objects with arbitrary reflectance properties. The second method, Rendering from a Sparse Set of Images, recovers both the illumination distribution and reflection parameters simultaneously from input images, providing even more compact representation for photorealistic image synthesis. With the leverage of assuming a specific reflection model and restricting the treatable objects, the latter method provides more flexibility in application, i.e., fewer input images. In this dissertation, we present the theory of these methods, and report on the results obtained by applying the methods to real world objects. Acknowledgements I would first like to thank Katsushi Ikeuchi, my advisor and my mentor. Had I not met him, I would never have dreamed of pursuing my career as a researcher. Five years of interaction with him have brought me tremendous experience that I could never have gained elsewhere. Being his first student after he came back to Japan was a prestigious honor for me. Starting from only a handful people, our lab has now grown faster than Moore's law. If I am responsible for even a small bit of this prosperity, I am more than delighted. I gratefully thank him for giving me the freedom to explore whatever I was interested in and for having an open door whenever I needed it. Yoichi Sato, my former co-advisor and co-worker, deserves special thanks for helping me make a good start. His practical advices have always helped me. I would also like to express my deepest gratitude to Zhengyou Zhang, my mentor at Microsoft Research Redmond. I was very fortunate to have him as my mentor; he gave me the freedom to explore my ideas and to conduct research as an independent researcher. I have learnt a lot from his passion … |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.cs.drexel.edu/~kon/pubs/KNishino_PhD.pdf |
| Alternate Webpage(s) | http://www.cvl.iis.u-tokyo.ac.jp/thesis/doctor/KNishino_PhD.pdf |
| Alternate Webpage(s) | https://www.cs.drexel.edu/~kon/publication/KNishino_PhD.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
