Search

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. [ABSTRACT FROM AUTHOR]

By observing that the recently presented Incremental Rotation Averaging (IRA) suffers from drifting and efficiency problems in large-scale situations, it is upgraded in this work to possess stronger scalability in both accuracy and efficiency based on the thought of divide and conquer. This upgraded version is termed as IRA++. Specifically, the original Epipolar-geometry Graph (EG) is clustered into several sub-graphs and inner-rotation averaging is distributedly performed in each of them with IRA at first. Then, the relative rotation between each pair of inner-sub-EG coordinate systems is distributedly estimated by a voting-based single rotation averaging method. Subsequently, IRA-based inter-rotation averaging is performed to obtain the absolute rotation of each inner-sub-EG coordinate system. And finally, the absolute rotations of all the cameras in the original EG are globally aligned and optimized to get the final rotation averaging result. Comprehensive evaluations on the 1DSfM, Campus, and San Francisco datasets demonstrate the advantages of our proposed IRA++ over IRA and several other state-of-the-art rotation averaging methods in both efficiency and accuracy, especially the accuracy in noise-polluted and efficiency in large-scale situations. [ABSTRACT FROM AUTHOR]

With the popularization of smartphones, larger collection of videos with high quality is available, which makes the scale of scene reconstruction increase dramatically. However, high-resolution video produces more match outliers, and high frame rate video brings more redundant images. To solve these problems, a tailor-made framework is proposed to realize an accurate and robust structure-from-motion based on monocular videos. The key ideas include two points: one is to use the spatial and temporal continuity of video sequences to improve the accuracy and robustness of reconstruction; the other is to use the redundancy of video sequences to improve the efficiency and scalability of system. Our technical contributions include an adaptive way to identify accurate loop matching pairs, a cluster-based camera registration algorithm, a local rotation averaging scheme to verify the pose estimate and a local images extension strategy to reboot the incremental reconstruction. In addition, our system can integrate data from different video sequences, allowing multiple videos to be simultaneously reconstructed. Extensive experiments on both indoor and outdoor monocular videos demonstrate that our method outperforms the state-of-the-art approaches in robustness, accuracy and scalability. [ABSTRACT FROM AUTHOR]

Completeness and accuracy are two important factors in image-based indoor scene 3D reconstruction. Thus, an efficient image capturing scheme that could completely cover the scene, and a robust reconstruction method that could accurately reconstruct the scene are required. To this end, in this article we propose a new pipeline for indoor scene capturing and reconstruction using a mini drone and a ground robot, which takes both capturing completeness and reconstruction accuracy into consideration. First, we use a mini drone to capture aerial video of the indoor scene, from which a 3D aerial map is reconstructed. Then, the robot moving path is planned and a set of ground-view reference images are synthesized from the aerial map. After that, the robot enters the scene and captures ground video autonomously while using the reference images to locate its position during the movement. Finally, the ground and aerial images, which are adaptively extracted from the captured videos, are merged to reconstruct a complete and accurate indoor scene model. Experimental results on two indoor scenes demonstrate the effectiveness and robustness of our proposed indoor scene capturing and reconstruction pipeline. [ABSTRACT FROM AUTHOR]

In this paper, we propose a depth-map merging based multiple view stereo method for large-scale scenes which takes both accuracy and efficiency into account. In the proposed method, an efficient patch-based stereo matching process is used to generate depth-map at each image with acceptable errors, followed by a depth-map refinement process to enforce consistency over neighboring views. Compared to state-of-the-art methods, the proposed method can reconstruct quite accurate and dense point clouds with high computational efficiency. Besides, the proposed method could be easily parallelized at image level, i.e., each depth-map is computed individually, which makes it suitable for large-scale scene reconstruction with high resolution images. The accuracy and efficiency of the proposed method are evaluated quantitatively on benchmark data and qualitatively on large data sets. [ABSTRACT FROM AUTHOR]