Fast and robust local feature extraction for 3D reconstruction. (October 2018)

• Record Type: Journal Article
• Authors: Cao, Mingwei; Jia, Wei; Li, Yujie; Lv, Zhihan; Li, Lin; Zheng, Liping; Liu, Xiaoping
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 657
• Page End: 666
• Publication Date: 2018-10
• Subjects: Local feature -- 3D reconstruction -- Structure from motion -- Feature tracking -- Bundle adjustment; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.08.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Large-scale 3D reconstruction based on structure from motion (SFM) has attracted much attention from the computer vision community. Local feature extraction, which is typically used to locate correspondences between images, is one of the most important components of SFM. In this paper, we propose a fast and robust local feature extraction method, called OOD, for SFM-based 3D reconstruction. First, the adaptive and generic accelerated segment test (AGAST) detector is used to detect keypoints, and the image moment is used to define an orientation for these keypoints. Second, a novel descriptor based on the oriented difference of Gaussians is proposed to describe the keypoints, which can be computed directly from the difference of Gaussian image. Finally, a comprehensive evaluation is conducted using several benchmark datasets. Our experimental results indicate that the OOD method outperforms state-of-the-art methods.