Efficient Large-Scale Image-Based Modeling Using Divide and Conquer Strategy with Two-Layer Clustering
| Autor: | Li, Chia Min, 李佳旻 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Image-based modeling has been widely developed, but its computational cost and memory requirement are usually the main issues especially when we want to build a large-scale or even city-scale model but only personal computer can be used. In this paper, we propose an efficient large-scale image-based modeling approach which uses divide and conquer strategy with both location and image similarity. It allows normal people can easily build their own large-scale models with only a PC. In addition, unlike previous methods, which require users to select or input thousands of images manually, our approach only needs users to take multiple videos of the scene arbitrarily and it is easier and more practical for real applications. The main idea of this paper is using divide and conquer strategy based on both location information, i.e. GPS, and image similarity, i.e. feature matching and epipolar geometry. We firstly divide the videos into multiple small groups of video clips with location clustering and then divide these groups into further smaller clusters of images with image clustering. Finally, a framework of combining multiple small-scale models into a large-scale one is proposed. The two-layer clustering will decrease the computational requirements very much and the experimental results show its feasibility and accuracy. This is the first work using two-layer clustering based on both location and image information for large-scale model construction, to our best knowledge. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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