Iteratively Reweighted Midpoint Method for Fast Multiple View Triangulation

Yang, Kui; Fang, Wei; Zhao, Yan; Deng, Nianchun

doi:10.1109/lra.2019.2893022

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…The first one is the native multiple-view middle point (MVMP) method; it is currently the fastest method for multiple-view triangulation, though it is labeled as inaccurate. The second method is the unbiased midpoint method (IRMP) [24] . The third one is considered a benchmark by minimizing the cost function of Eq.…”

Section: Methodsmentioning

confidence: 99%

“…An obvious solution is to collect all of the observations of the 3D point when encountering a coming observation, and then a new multiple-view triangulation action is performed repeatedly [22] . However, it is a time-consuming process to perform multiple-view triangulation with iterative optimization [23,24] , especially in large-scale scenarios, where there are usually a significant number of scene points and cameras. The previous work [24] mainly paid attention to the triangulation issue after collecting all of the observational vectors at one time, lacking consideration of the INT and its convergence characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is a time-consuming process to perform multiple-view triangulation with iterative optimization [23,24] , especially in large-scale scenarios, where there are usually a significant number of scene points and cameras. The previous work [24] mainly paid attention to the triangulation issue after collecting all of the observational vectors at one time, lacking consideration of the INT and its convergence characteristics. Thus, instead of establishing an unpredictable iteration-based multiple-view cost function when encountering a new feature track one by one, a propagation-based INT method is proposed in this Letter to carve the 3D estimate only with the incoming observation.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2021

中国光学快报

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To balance the accuracy and efficiency in multiple-view triangulation with sequential images, a high-efficiency propagationbased incremental triangulation (INT) method, carving three-dimensional (3D) scene points by updating the incoming feature track one by one without iterations, is proposed. Based on the INT method, a more accurate iteration-limited INT method is also established with few iterations to bound the propagated errors, ensuring the accuracy of subsequent 3D reconstruction. Finally, experimental results demonstrate that the proposed methods can balance the efficiency and accuracy in different multiple-view INT situations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Untitled

2021

中国光学快报

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“…The MVMP (Multiple View Midpoint) method is des-cribed in detail in a recent work [24]. Suppose the optical center of the camera is O i , and the ray (unit vector) formed by the image point x i and the optical center O i is b i , then the distance from the spatial point X to ray b i can be expressed as…”

Section: B Midpoint Methodsmentioning

confidence: 99%

“…Therefore, some scholars have improved it on this basis. They believe that the error of the midpoint method mainly comes from the distance from the space point to the camera [24]. Therefore, a penalty factor w can be added to the objective function, so that the objective function becomes min e(X)…”

Section: B Midpoint Methodsmentioning

confidence: 99%

Multi-View Triangulation: Systematic Comparison and an Improved Method

Chen

Song

et al. 2020

IEEE Access

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Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices are known. However, in practice, multiple lines in 3D space do not intersect at one point because of noise. Then how to calculate the optimal 3D point of intersection becomes difficult. While there have been multiple methods trying to solve this problem, there is no systematic comparison between them. In this paper, we reviewed various currently existing variants of triangulation method and compared them through extensive experiments. The speed and accuracy of these methods have been compared using both synthetic and real datasets. We presented the results of experiments and summarized the advantages, limitations, and applicability of these methods so that to provide a guide for users when they need to choose an appropriate triangulation method for their given applications. Moreover, based on above analysis we proposed an improved method which shows better performance.

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