Structure from Motion with Line Segments Under Relaxed Endpoint Constraints

Superpixel-Based Feature Tracking for Structure from Motion

Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.

Structure from motion with efficient homography-based line matching

A novel structure from motion (SfM) pipeline that combines point features and line features together is presented in this paper. Line segments usually are more difficult to handle than points in SfM due to the instability of their end points and the property of being unconstrained by epipolar geometry. Our proposed method first implements a very robust and efficient line segment matching by applying homography and optical flow tracking. Global homography and multiple homography schemes are combined to get a better effect. By virtue of sampling lines, line matching is converted to point matching. A unified global SfM pipeline is then available for both lines and points. In the end, the proposed method is evaluated through various datasets. The result shows that our line-based SfM can generate accurate 3D models with lines and give meaningful representation for a man-made environment.