Contents 1. Introduction
Structure-from-Motion (SfM) from unordered images has seen tremendous evolution over the years. The early self-calibrating metric reconstruction systems [42], [6], [19], [16], [46] served as the foundation for the first systems on unordered Internet photo collections [47], [53] and urban scenes [45]. Inspired by these works, increasingly large-scale reconstruction systems have been developed for hundreds of thousands [1] and millions [20], [62], [51], [50] to recently a hundred million Internet photos [30]. A variety of SfM strategies have been proposed including incremental [53], [1], [20], [62], hierarchical [23], and global approaches [14], [61], [56]. Arguably, incremental SfM is the most popular strategy for reconstruction of unordered photo collections. Despite its widespread use, we still have not accomplished to design a truly general-purpose SfM system. While the existing systems have advanced the state of the art tremendously, robustness, accuracy, completeness, and scalability remain the key problems in incremental SfM that prevent its use as a general-purpose method. In this paper, we propose a new SfM algorithm to approach this ultimate goal. The new method is evaluated on a variety of challenging datasets and the code is contributed to the research community as an open-source implementation named COLMAP available at https://github.com/colmap/colmap.
Result of rome with 21K registered out of 75K images.
