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Structure from motion (SfM) is used to recover camera poses and the sparse structure of real scenes from multiview images. SfM methods construct a view‐graph from the matching relationships of images. Redundancy and incorrect edges are usually observed in it. Redundancy inhibits the efficiency and incorrect edges result in the misalignment of structures. In addition, the uneven distribution of vertices usually affects the global accuracy. To address these problems, we propose a coarse‐to‐fine approach in which the poses of an extracted key‐subset of images are first computed and then all remaining images are oriented. The core of this approach is view‐graph key‐subset extraction, which not only prunes redundant data and incorrect edges but also obtains properly distributed key‐subset vertices. The extraction approach is based on a replaceability score and an iteration‐update strategy. In this way, only vertices with high SfM importance are preserved in the key‐subset. Different public datasets are used to evaluate our approach. Due to the absence of ground‐truth camera poses in large‐scale datasets, we present new datasets with accurate camera poses and point clouds. The results demonstrate that our approach greatly increases the efficiency of SfM. Furthermore, the robustness and accuracy can be improved. [ABSTRACT FROM AUTHOR]

Most of the current techniques for concrete bridge inspection are based on human visual interpretation, which often is dangerous and time-consuming. To address this problem, we introduce in this paper a newly developed vehicle-based robot inspection system that can automatically capture thousands of bottom surface images with a group of high-resolution industrial cameras, which are then stitched into a single composite image. However, traditional image stitching methods generally fail with large drift due to the great number (more than 2000) and sparse texture of linearly distributed images in sequence. Therefore, a novel image stitching method was developed for our robot inspection system, which combines both the 2D image point features and the 3D line features to reduce the drift. First, the bottom surface images are arranged into different strips based on their acquisition order and rough poses, and images in a single strip are divided into several groups. Then, the proposed image stitching method is performed in a bottom-up way, as follows: 1) the images within a single group initially are aligned via their point and line features; 2) the groups within a single strip are then stitched together via a homographic refinement procedure; 3) the strips are aligned into a single composite image that completely covers the bottom surface of the bridge; and 4) after all the stitching procedure are complete, a multi-band blending algorithm is applied to generate the mosaicked panorama as seamlessly as possible. The experimental results on a set of representative images acquired from the bottom surfaces of a real bridge demonstrate the capabilities and the limitations of the proposed approach. [ABSTRACT FROM AUTHOR]

Structure from motion (SfM) recovers scene structures and camera poses based on feature matching, and faces challenges from ambiguous scenes. There are a large number of ambiguous scenes in real environment, which contain many duplicate structures and textures. The ambiguity leads to incorrect feature matches between images with similar appearance, and makes geometric misalignment in SfM. To address this problem, recent methods have focused on investigating the inconsistencies in feature topology among multi-view images. However, the feature topology is directly derived from 2D images. Thus, it is susceptible to feature occlusion caused by changes in perspective. Therefore, we propose a new method that disambiguates scenes using pose consistency rather than feature consistency. The pose consistency is conducted in 3D geometric space which is less sensitive to feature occlusion. Thus, the pose consistency is more robust than feature consistency. Our core motivation lies that the incorrect matches between ambiguous images will cause pose deviation from the global poses generated by correct matches. To detect this pose deviation, we first combine local and global information of the scene to generate the global reliable camera poses. The local information of each image is obtained by image clustering, and it strengthens the global information that is represented as the verified maximum spanning tree of clusters. Then, the global poses serve as the reference for further pose consistency verification. The global poses also enable us to perform both rotation and translation consistency verification for uncertain matches. During the pose consistency verification, the pose deviation calculated on image-level may be too small to be noticed. Thus, we propose to perform pose consistency verification at cluster-level instead of image-level to amplify the pose deviation. In the experiments, we compared our approach with several state-of-the-art methods, including COLMAP, Geodesic-SfM and TC-SfM, on both ambiguous and regular datasets. The results demonstrate that our approach achieves the best robustness, only our approach succeeds on all ambiguous image sequences (14/14). The quantitative evaluation results on image sequences with ground truth also show that our approach achieves the best accuracy (average RMSE of translation = 0.109, average RMSE of rotation = 0.827) among all methods. The source code of our approach is publicly available at https://github.com/gongyeted/MA-SfM. [ABSTRACT FROM AUTHOR]