Structure from motion using dense CNN features with keypoint relocalization

• Published in: IPSJ transactions on computer vision and applications Vol. 10; no. 1; pp. 1 - 7
• Main Authors: Widya, Aji Resindra, Torii, Akihiko, Okutomi, Masatoshi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 31.05.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computer Imaging; Computer Science; Express Paper; Feature extraction; Image Processing and Computer Vision; Imagery; Motion perception; Pattern Recognition and Graphics; Pixels; Reconstruction; Reproducibility; Robotics and Automation; Spatial resolution; User Interfaces and Human Computer Interaction; Vision; Feature detection and description; Feature matching; 3D reconstruction; Structure from Motion
• ISSN: 1882-6695; 1882-6695
• DOI: 10.1186/s41074-018-0042-y

Structure from motion (SfM) using imagery that involves extreme appearance changes is yet a challenging task due to a loss of feature repeatability. Using feature correspondences obtained by matching densely extracted convolutional neural network (CNN) features significantly improves the SfM reconstruction capability. However, the reconstruction accuracy is limited by the spatial resolution of the extracted CNN features which is not even pixel-level accuracy in the existing approach. Providing dense feature matches with precise keypoint positions is not trivial because of memory limitation and computational burden of dense features. To achieve accurate SfM reconstruction with highly repeatable dense features, we propose an SfM pipeline that uses dense CNN features with relocalization of keypoint position that can efficiently and accurately provide pixel-level feature correspondences. Then, we demonstrate on the Aachen Day-Night dataset that the proposed SfM using dense CNN features with the keypoint relocalization outperforms a state-of-the-art SfM (COLMAP using RootSIFT) by a large margin.