Fast bundle adjustment using adaptive moment estimation

Tiexin Liu; Liheng Bian; Xianbin Cao; Jun Zhang

doi:10.1117/12.2538745

18 November 2019 Fast bundle adjustment using adaptive moment estimation

Tiexin Liu, Liheng Bian, Xianbin Cao, Jun Zhang

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 111871R (2019) https://doi.org/10.1117/12.2538745
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

Bundle adjustment (BA) is an important task for feature matching in multiple applications such as image stitching and position mapping. It aims to reconstruct the 8-parameter homography matrix, which is used for perspective transformation among different images. The existing algorithms such as the Levenberg-Marquardt (LM) algorithm and the Gauss{Newton (GN) algorithm require much computation and a large number of iterations. To accelerate reconstruction speed, here we propose a novel BA algorithm based on adaptive moment estimation (Adam). The Adam solver uses the mean and uncentered variance of the gradients in the previous iterations to dynamically adjust the gradient direction of the current iteration, which improves reconstruction quality and increases convergence speed. Besides, it requires only the first derivate calculation, and thus obtains low computational complexity. Both simulations and experiments validate that the proposed method converges faster than the conventional BA methods.

Citation Download Citation

Tiexin Liu, Liheng Bian, Xianbin Cao, and Jun Zhang "Fast bundle adjustment using adaptive moment estimation", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111871R (18 November 2019); https://doi.org/10.1117/12.2538745

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KEYWORDS

Algorithm development

Reconstruction algorithms

3D image reconstruction

Electronics

3D image processing

Associative arrays

Astatine

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