Efficient incremental structure from motion (ISfM) has become the core technique for (unmanned aerial vehicle, UAV) image orientation. However, the characteristics of large volume, high overlap, and high resolution cause the deficiency in match pair retrieval and the accumulated error and low efficiency in bundle adjustment (BA) optimization, which degenerate its performance for large-scale scenes. This study proposes a parallel SfM for UAV images via global descriptors and graph-based indexing. On the one hand, to cope with the deficiency caused by a large number of local descriptors and the large size of a codebook, an efficient match pair retrieval is designed via the global descriptor and graph-based indexing, which could dra-matically accelerate feature matching; on the other hand, to address the deficiency of correspondence searching and low accura-cy of transformation estimation in parallel SfM, this study designs an efficient cluster merging algorithm based on the on-de-mand correspondence graph and bi-directional reprojection error, which achieves efficient and accurate parallel SfM. The pro-posed algorithm is verified by using three UAV datasets, and the experimental results demonstrate that the proposed method can increase match pair retrieval with speedup ratios ranging from 36 to 108, and dramatically improves the SfM efficiency with the speedup ratio better than 30 and with the comparative accuracy. The accuracy of relative and absolute orientation is compar-ative to that of traditional methods.
关键词
数字摄影测量/无人机遥感/运动恢复结构/影像检索
Key words
digital photogrammetry/UAV remote sensing/structure from motion/image retrieval
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