21 February 2018 Optimization of incremental structure from motion combining a random k-d forest and pHash for unordered images in a complex scene
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Abstract
On the basis of today’s popular virtual reality and scientific visualization, three-dimensional (3-D) reconstruction is widely used in disaster relief, virtual shopping, reconstruction of cultural relics, etc. In the traditional incremental structure from motion (incremental SFM) method, the time cost of the matching is one of the main factors restricting the popularization of this method. To make the whole matching process more efficient, we propose a preprocessing method before the matching process: (1) we first construct a random k-d forest with the large-scale scale-invariant feature transform features in the images and combine this with the pHash method to obtain a value of relatedness, (2) we then construct a connected weighted graph based on the relatedness value, and (3) we finally obtain a planned sequence of adding images according to the principle of the minimum spanning tree. On this basis, we attempt to thin the minimum spanning tree to reduce the number of matchings and ensure that the images are well distributed. The experimental results show a great reduction in the number of matchings with enough object points, with only a small influence on the inner stability, which proves that this method can quickly and reliably improve the efficiency of the SFM method with unordered multiview images in complex scenes.
© 2018 SPIE and IS&T
Zongqian Zhan, Zongqian Zhan, Chendong Wang, Chendong Wang, Xin Wang, Xin Wang, Yi Liu, Yi Liu, } "Optimization of incremental structure from motion combining a random k-d forest and pHash for unordered images in a complex scene," Journal of Electronic Imaging 27(1), 013024 (21 February 2018). https://doi.org/10.1117/1.JEI.27.1.013024 . Submission: Received: 27 August 2017; Accepted: 19 January 2018
Received: 27 August 2017; Accepted: 19 January 2018; Published: 21 February 2018
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