Fast Iterative Reconstruction Based on Condensed Hierarchy Tree

Based on the traditional iterative reconstruction workflow, a fast iterative reconstruction algorithm FIRA is proposed. First, using the image feature points extracted by SIFT algorithm, calculation of image similarity based on the minimum hash algorithm in LSH model is performed. Then, the iteration order is specified through hierarchical clustering. In the iterative process, the orientation estimation of images is carried through the clustering result coming from hierarchical tree. The optimization of parameter estimation is performed by bundle adjustment, and finally produce 3d mesh models. The experimental results show that the method could bring high efficiency and eliminate the accumulated error of adjustment calculation.

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