## 2 Related Work
### Structure from Motion  
Recent years, many 3D multi-view 3D reconstruction systems based on SFM technique have been proposed. For example, [PERSON] et al. [10] designed and implemented an excellent 3D reconstruction system, called Bundler, to reconstruct spare point-cloud model from unordered image collections. In the Bundler system, the authors employ scale invariant feature transform (SIFT) [11] to detect keypoints and compute descriptors, and use brute-force matching (BFM) strategy to match descriptors for image pair. However, owing to the usage of SIFT and BFM, the Bundler system has high computation cost. To save the computation time for 3D reconstruction based SFM, [PERSON] et al. [12] developed a Visual SFM (VSFM) system based on Bundler, which use SIFTGPU to detect keypoints and compute descriptors for saving computation time. [PERSON] et al. [52] presented a novel SFM pipeline, which estimates motion and wiry 3D point clouds from imaged line segments across multiple views. The proposed SFM system tackle the problem of unstable endpoints by using relaxed constraints on their positions, both during feature tracking and in the bundle adjustment stage. [PERSON] et al. [53] introduced the distributed camera model for 3D reconstruction based on SFM technique, in which, the proposed model describes image observations in terms of light rays with ray origins and directions rather than pixels. As a result, the camera model can describe a single camera or multiple cameras simultaneously as the collection of all light rays observed.  
Based on the successes in solving for global camera rotations using averaging technique, [PERSON] et al. [54] proposed a simple, effective method for solving SFM problems by averaging epipolar geometries. The proposed unstructured SFM system (1 DSFM) can overcome several disadvantages of existing sequential SFM. [PERSON] et al. [55] proposed a novel global calibration approach based on the global fusion of relative motions between image pairs for robust, accurate and scalable SFM. After an efficient contrario trifocal tensor estimation, the authors define an efficient translation registration method to recover accurate positions. Besides accurate camera position, [PERSON] et al. use KAZE [29] feature to detect keypoints in feature tracking, then resulting in a high-precision score. Based on optimized viewgraph, [PERSON] et al. [56] designed and implemented an excellent SFM system, named Theia-SFM, to produce compact and accurate point-cloud model for both indoor and outdoor scenes. To recover the location of an object, [PERSON] et al. [57] designed a scalable SFM system by utilizing ShapeFit and ShapeKick, even in the presence of adversarial outliers. [PERSON] et al. [58] proposed a novel solution for 3D reconstruction based on SFM to reconstruct the inside and the outside of a building into a single model by utilizing the semantic information, in which, novel cost function is proposed to determine the best alignment. To solve the degeneracies introduced by rolling shutter camera models, [PERSON] et al. [59] show that many common camera configurations such as cameras with parallel readout directions, become critical and allow for a large class of ambiguities in 3D reconstruction based on SFM technique.  
With the development of the depth camera, such as Kinect and RealSense, many RGBD datasets are publicly available for 3D reconstruction. [PERSON] et al. [60] developed RGBD-SFM system to produce dense point cloud model from RGBD images. Recently, [PERSON] et al. [61] hold that SFM methods can be broadly categorized as incremental or global according to their ways to estimate initial camera poses. They proposed a unified framework to tackle the issues of efficiency, accuracy, and robustness, and developed a hybrid structure from motion (HSFM) system.