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Evaluation of Local Features for Structure from Motion

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Abstract

Structure from motion (SFM) is an effective approach for reconstructing large-scale 3D scene from multiple images. In this field, many local feature methods have been proposed to detect feature point and compute descriptor. For designing a robust SFM system, how to select a good feature from existing methods is an important problem. In this paper, we aim to help different users for making decision by an experimental way for large-scale 3D reconstruction where many high resolution images are captured. To this end, we make a comprehensive evaluation of several local features on the ground truth datasets. Experimental results show that SIFT and SURF have a better performance than that of some binary features such as ORB and BRISK.

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Acknowledgments

This work is partly supported by the grants of the National Science Foundation of China, Nos. 61673157, and 61402018, the grant of the Natural Science Foundation of Anhui Province, Nos. KJ2014ZD27 and JZ2015AKZR0664, and also supported by the National Key Research and Development Plan under Grant No. 2016YFC0800100.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China

    Mingwei Cao, Li Cao, Wei Jia, Liping Zheng & Xiaoping Liu

  2. Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei, Anhui, China

    Mingwei Cao, Li Cao, Wei Jia, Liping Zheng & Xiaoping Liu

  3. Faculty of engineering, Fukuoka University, Fukuoka, Japan

    Yujie Li

  4. School of Data Science and Software Engineering, Qingdao University, Qingdao, Shandong, China

    Zhihan Lv

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  1. Mingwei Cao
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Correspondence to Li Cao.

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Cao, M., Cao, L., Jia, W. et al. Evaluation of Local Features for Structure from Motion. Multimed Tools Appl 77, 10979–10993 (2018). https://doi.org/10.1007/s11042-018-5864-1

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