An efficient solution to the five-point relative pose problem

By David Nistér. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

2,101 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/26f47782d667a3b4cd6061a844dbfac601306fed

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Recent developments on direct relative orientation (2006, methodology): “It has also been found Nistér (2004) that there are pathological cases with ten real solutions that are well separated in parameter space and have all the scene points in front of the cameras.”
Outdoor SLAM using visual appearance and laser ranging (2006, methodology): “Given two image views of the same scene, five point of correspondences are selected for use in an implementation of the “five point algorithm ” described in [18] which, notably, is capable of dealing with coplanar correspondence points.”
OpenMVG: Open Multiple View Geometry (2016, background): “– Relative pose from pairs of image-image matching points, such as homography (4-point algorithm [6] for transform of planar scene or scene viewed under pure rotation), fundamental matrix (7/8-point algorithm [6], in case of ignorance of camera internal parameters), essential matrix (5-point [8], in”
Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques (2011, methodology): “The camera parameters for this pair are estimated using Nistér's five point algorithm [36], and then the feature points visible in the image pair are triangulated.”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, methodology): “By enforcing the epipolar constraint, the pose from two views can be computed by the 8-point [53] or the 5-point algorithm [116], while the trifocal tensor [156] can be utilized if three views are available.”
SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion (2013, positive): “For matching pairs, we estimate relative pose using the five-point algorithm [32] followed by bundle adjustment.”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “Table 4 reports the absolute trajectory errors of Kimera when using 5-point RANSAC, 2-point RANSAC, and when using 2-point RANSAC and IMU-aware feature tracking (label: DVIO).”
Stable real-time 3D tracking using online and offline information (2004, methodology): “To this end, we have formulated the tracking problem as one of local bundle adjustment in such a way that it can be solved very quickly.”
BA-Net: Dense Bundle Adjustment Network (2018, methodology): “We further compare with conventional geometric (Nister, 2004; Agarwal et al.) and photometric (Engel et al., 2014) BA.”
Modeling the World from Internet Photo Collections (2008, methodology): “The camera parameters for this pair are estimated using Nistér's implementation of the five point algorithm (Nistér 2004), 10 then the tracks visible in the two images are triangulated.”
Visual odometry (2004, methodology): “Estimate the relative poses between three of the frames using the 5-point algorithm [13] and preemptive RANSAC [15] followed by iterative refinement.”
Disambiguating visual relations using loop constraints (2010, methodology): “Geometric verification based on essential matrix computation [18] is applied on these image pairs using either the known intrinsics or approximate values from the EXIF tags.”
Two years of Visual Odometry on the Mars Exploration Rovers (2007, methodology): “have reported a successful real-time Visual Odometry implementation (Nistér et al., 2006; Nistér, 2004).”
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC (2009, background): “For unconstrained motion of a calibrated camera this would be 5 point correspondences [19], [20].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “The problem of arbitrary absolute scale affects all monocular setups – from SLAM systems like (Davison et al., 2007) to pose estimation in n-point algorithms (Nistér, 2004), or optical flow based approaches (Hamel and Mahony, 2002).”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “The eight-point algorithm (Longuet-Higgins (1981)) is a simple solution working with calibrated and uncalibrated cameras whereas the five-point algorithm (Nistér (2004)) is a minimal case solution which only applies to the scenario of calibrated cameras.”
Building Rome on a Cloudless Day (2010, background): “The latter estimate typically approximates the true focal length within the error bounds of successfully executing the five-point method [31].”