关键词: 辅助信息, 图划分, 视图聚类, 三维重建

Abstract: One of the most efficient ways to tackle the scalability problem in large scene reconstruction is to break apart the scene into a number of sub-problems, then reconstruct each sub-problem independently, and merge the partial reconstructions finally. Image clustering without any camera or scene prior information is a difficult problem in 3D reconstruction. Image clustering is inherently time consuming, and generally no satisfactory results can be achieved. This paper explores how to use the auxiliary information of cameras which is non-accurate and neglected but available, and substantially simplifies the image clustering in 3D scene reconstruction. Firstly, the view-overlap is computed, then the view-overlap based clustering approach is proposed, and finally the clusters are independently reconstructed and merged. The experiments are done on several sets of real images, the results show that compared with the image retrieval based method and Samantha method, the scene reconstruction via camera auxiliary information performs satisfactorily in terms of efficiency, robustness and scalability.

Key words: auxiliary information, graph partitioning, view clustering, 3D reconstruction