Multistage SFM: Revisiting Incremental Structure from Motion

Shah, Rajvi; Deshpande, Aditya; Narayanan, P. J.

doi:10.1109/3dv.2014.95

Cited by 26 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Wu (2013) proposed a match selection method, so called pre-emptive matching algorithm, to quickly judge whether or not two images could be matched. Shah et al (2014) proposed a similar method which begins with building a coarse 3D reconstruction using a fraction of high-scale features of given images. A related approach has been described in Mayer (2003), Lerma et al (2013) and Liu et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

Mousavi

Varshosaz

Remondino

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Image orientation is a fundamental task in photogrammetric applications and it is performed by extracting keypoints with hand-crafted or learning-based methods, generating tie points among the images and running a bundle adjustment procedure. Nowadays, due to large number of extracted keypoints, tie point filtering approaches attempt to eliminate redundant tie points in order to increase accuracy and reduce processing time. This paper presents the results of an investigation concerning tie points impact on bundle adjustment results. Simulations and real data are processed in Australis and DBAT to evaluate different affecting factors, including tie point numbers, location accuracy, distribution and multiplicity. Achieved results show that increasing the amount of tie points improve the quality of bundle adjustment results, provided that the tie points are well-distributed on the image. Furthermore, bundle adjustment quality is improved as the multiplicity of tie points increases and their location uncertainty decrease. Based on simulation results, some suggestions for accurate tie points filtering in typical UAV photogrammetry blocks cases are derived.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

Mousavi

Varshosaz

Remondino

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…A variety of SfM methods have been proposed so far and can be basically categorized as incremental (e.g., Snavely et al, 2006;Frahm et al, 2010;Agarwal et al, 2011;Wu, 2013;Shah et al, 2014), hierarchical (e.g., Farenzena et al, 2009;Gherardi et al, 2010;Ni and Dellaert, 2012) and global methods (e.g., Arie-Nachimson et al, 2012;Jiang et al, 2013;Moulon et al, 2013;Wilson and Snavely, 2014;Cui and Tan, 2015). Among them, incremental SfM methods are the most widely used ones.…”

Section: Introductionmentioning

confidence: 99%

A Photogrammetry-Based Structure From Motion Algorithm Using Robust Iterative Bundle Adjustment Techniques

Verykokou

Ioannidis

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> The purpose of this paper is the presentation of a novel algorithm for automatic estimation of the exterior orientation parameters of image datasets, which can be applied in the case that the scene depicted in the images has a planar surface (e.g., roof of a building). The algorithm requires the measurement of four coplanar ground control points (GCPs) in only one image. It uses a template matching method combined with a homography-based technique for transfer of the GCPs in another image, along with an incremental photogrammetry-based Structure from Motion (SfM) workflow, coupled with robust iterative bundle adjustment methods that reject any remaining outliers, which have passed through the checks and geometric constraints imposed during the image matching procedure. Its main steps consist of (i) determination of overlapping images without the need for GPS/INS data; (ii) image matching and feature tracking; (iii) estimation of the exterior orientation parameters of a starting image pair; and (iv) photogrammetry-based SfM combined with iterative bundle adjustment methods. A developed software solution implementing the proposed algorithm was tested using a set of UAV oblique images. Several tests were performed for the assessment of the errors and comparisons with well-established commercial software were made, in terms of automation and correctness of the computed exterior orientation parameters. The results show that the estimated orientation parameters via the proposed solution have comparable accuracy with those ones computed through the commercial software using the highest possible accuracy settings; in addition, double manual work was required by the commercial software compared to the proposed solution.</p>

show abstract

“…e feature points of an image are mapped to other images by the matrix F and near the epipolar line. In this case, F is called the fundamental matrix, and the rotation and translation [R|t] vectors of the camera can be constructed from F [4][5][6][7][8][9][10][11]. is allows us to identify camera movement.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Location-Based Positioning System Using Stereo Vision with the Drone Camera

Jin

Lee

2018

Mobile Information Systems

View full text Add to dashboard Cite

Stereo vision is used to reconstruct 3D information of the space by estimating the depth value from the simulation of human eyes. Spatial restoration can be used as a means of location estimation in an indoor area, which is impossible to accomplish using the relative location estimation technology, GPS. By mapping the real world in virtual space, it is feasible to clear the boundary between real space and virtual space. This paper presents a method to control the drone indoors through a positioning system using Structure from Motion algorithm (SfM). SfM calculates the relative relationship between cameras based on images to be acquired from various locations and obtains disparity to enable restoration of 3D space. First, the 3D virtual space is reconstructed using several photographs taken from an indoor environment. Second, the real-time drone position is determined by comparing the 3D virtual space camera with the image displayed on the drone camera. In this case, if the direction of the virtual camera used for 3D virtual space construction is the same as the amount of yaw rotation of the drone, it is possible to quickly find the same position as the image seen in the real drone camera in the virtual space. As a result, if the scale of the actual camera image and the virtual camera image is 1 : 1 matched, then it is possible to know that the drone is in the position of the virtual camera. The proposed indoor location-based drone controlling method can be applied to various drone applications such as group flight in an indoor environment because of its ability to fly the drone without the use of the traditional remote-control and flight trajectory programming.

show abstract

Multistage SFM: Revisiting Incremental Structure from Motion

Cited by 26 publications

References 24 publications

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

Evaluating Tie Points Distribution, Multiplicity and Number on the Accuracy of Uav Photogrammetry Blocks

A Photogrammetry-Based Structure From Motion Algorithm Using Robust Iterative Bundle Adjustment Techniques

An Indoor Location-Based Positioning System Using Stereo Vision with the Drone Camera

Contact Info

Product

Resources

About