Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state of the art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://github.com/openxrlab/xrsfm

翻译：运动恢复结构是一种通过图像采集获取场景结构的技术，是计算机视觉中的基础问题。对于无序互联网图像，由于缺乏图像重叠的先验知识，SfM的执行速度非常缓慢。对于有序图像，在已知相邻帧之间存在大量重叠的情况下，SfM可采用多种仅适用于有序数据的加速策略。为进一步提升重建效率并弥合这两类数据策略之间的鸿沟，本文提出了一种高效的基于共视性的增量式SfM方法。与以往方法不同，我们利用共视性和配准依赖关系来描述图像连接，该方法适用于任意数据类型。基于这一通用图像连接，我们提出了一个统一框架，能够高效重建有序图像、无序图像以及两者的混合数据。在无序图像和混合数据上的实验验证了所提方法的有效性，其特征匹配速度比现有最优方法快三倍，重建速度提高一个数量级且不牺牲精度。源代码已开源发布于https://github.com/openxrlab/xrsfm