An efficient solution to the five-point relative pose problem

Nistér, D.

doi:10.1109/cvpr.2003.1211470

Cited by 788 publications

(1,192 citation statements)

References 24 publications

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“…Unlike previous works [21,22,28,29], we execute single RANSAC algorithm and then postprocess obtained inliers. The idea is the following: If we executed a RANSAC based algorithm on all N 7 7-tuples chosen out of N tentative matches, then we would obtain all maximal inlier sets.…”

Section: The Robust Methods For Focal Length Estimationmentioning

confidence: 99%

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Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Bujnak¹,

Kúkelová

Pajdla

2010

Computer Vision – ACCV 2009

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Abstract. We propose a new robust focal length estimation method in multi-view structure from motion from unordered data sets, e.g. downloaded from the Flickr database, where jpeg-exif headers are often incorrect or missing. The method is based on a combination of RANSAC with weighted kernel voting and can use any algorithm for estimating epipolar geometry and unknown focal lengths. We demonstrate by experiments with synthetic and real data that the method produces reliable focal length estimates which are better than estimates obtained using RANSAC or kernel voting alone and which are in most real situations very close to the ground truth. An important feature of this method is the ability to detect image pairs close to critical configurations or the cases when the focal length can't be reliably estimated 3 .

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Section: The Robust Methods For Focal Length Estimationmentioning

confidence: 99%

“…Efficient solvers, e.g. the 5-pt relative pose solver for calibrated cameras [21,26], also helped in developing such Structure from Motion (SFM) pipelines.…”

Section: Introductionmentioning

confidence: 99%

Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Bujnak¹,

Kúkelová

Pajdla

2010

Computer Vision – ACCV 2009

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“…In this research, a closed-form solution, which is similar to the one proposed by Nistér, (2004) for the five-point approach, is adopted for the estimation of the Essential Matrix. More specifically, a second-order polynomial, which provides two possible estimates for the simplified Essential Matrix as shown in Equation 5, can be established.…”

Section: Two-point Approachmentioning

confidence: 99%

“…In the meantime, several fivepoint approaches have been proposed as alternatives to the eightpoint approach (Faugeras and Maybank, 1990;Philip, 1998). The most efficient five-point algorithm is the one proposed by Nistér, (2004). Compared to the eight-point approach, the five-point approaches take into account the inherent constraints among the elements of the Essential Matrix.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

He¹,

Habib²

2016

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

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ABSTRACT:Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.

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“…On the other hand, reconstruction methods (e.g. those based on SFM) [26,8,31,24,32] produce metric recovery of object and scene 3D structure (3D point clouds) but are mostly unable to infer the semantic content of its components ( Figure 1(c)). …”

Section: Introductionmentioning

confidence: 99%

Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction

Bao

Savarese

2012

Lecture Notes in Computer Science

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Abstract. Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this chapter, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in a scene. These properties allow to jointly estimate the structure of the scene, the camera parameters as well as the 3D locations, poses, and categories of objects in a scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-pointbased SFM algorithms, iii) improve object detections given multiple uncalibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets -LiDAR cars, street-view pedestrians, and Kinect office desktop -verify our theoretical claims.

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An efficient solution to the five-point relative pose problem

Cited by 788 publications

References 24 publications

Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction

Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction

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