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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools
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Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools

机译：从大规模UAV拍摄的运动的高效结构：SFM工具的审查和比较

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摘要

Unmanned aerial vehicle (UAV) images have gained extensive attention in varying fields, and the Structure from Motion (SfM) technique has become the gold standard for aerial triangulation of UAV images. With increasing data volume caused by the use of multi-view and high-resolution imaging systems and the enhancement of UAV platform's endurance, the capability for orientation of large-scale UAV images is becoming a prominent and necessary feature for SfM-based solutions. A classical SfM pipeline consists of three major steps, i.e., (i) feature extraction for an individual image, (ii) feature matching for each image pair, and (iii) parameter solving based on iterative bundle adjustment. Most of the time costs are consumed in the second and third steps. This can be explained from three main aspects. First, for feature matching the large number of images and high overlapping degrees cause high combinational complexity of match pairs. Second, the efficiency of commonly utilized techniques for outlier removal would be seriously degenerated because of high outlier ratios of initial matches. Third, for parameter solving of camera poses and scene structures, the iterative execution of bundle adjustment (BA) leads to high computational costs in the incremental SfM workflow. Thus, this paper gives a systematic survey of the state-of-the-art for match pair selection from both ordered and unordered datasets, for outlier removal of initial matches dominated by outliers, and for efficiency improvement of BA, and conducts an experimental evaluation for six well-known SfM-based software packages on UAV image orientation.
机译：无人驾驶飞行器（UAV）图像在不同的领域中获得了广泛的关注，并且来自运动（SFM）技术的结构已成为UAV图像的空中三角扫描的金标准。随着使用多视图和高分辨率成像系统的使用引起的数据量以及UAV平台的增强，大规模UAV图像的方向的能力成为基于SFM的解决方案的突出和必要的特征。经典SFM管道由三个主要步骤组成，即（i）特征提取用于单个图像，（ii）对每个图像对的特征匹配，（iii）基于迭代捆绑调整的参数解决。大多数时间成本在第二步和第三步消耗。这可以从三个主要方面解释。首先，对于匹配大量图像和高重叠度的特征导致匹配对的高组合复杂性。其次，由于初始匹配的高比率比率高，所以常见的拆除技术的常见技术的效率将严重退化。第三，对于摄像机姿势和场景结构的参数解决，捆绑调整（BA）的迭代执行导致增量SFM工作流程的高计算成本。因此，本文给出了对来自订购和无序数据集的最先进的最先进的最先进的系统调查，用于删除由异常值主导的初始匹配以及BA的效率提高，并进行实验评估对于UAV映像方向的六种基于SFM的软件包。

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