An efficient fundamental matrix estimation method for wide baseline images

Xiao, Chun-Bao; Feng, Da‐Zheng; Yuan, Ming‐Dong

doi:10.1007/s10044-016-0561-z

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…Two datasets were used in the experiment: one is the real dataset, and the other is a simulated dataset containing Gaussian noise and outliers. The proposed method is compared with ISSO [30], EGEC [31] and RANSAC [26] methods. In the subsequent experiments, the mean epipolar geometry distance and the mean distance between feature points and the intersection points of epipolar lines are used as the evaluation criteria to evaluate the accuracy of various methods.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Liang

Huo

2022

Sensors

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A fundamental matrix estimation based on matching points is a critical problem in epipolar geometry. In this paper, a global fundamental matrix estimation method based on inlier updating is proposed. Firstly, the coplanar constraint was incorporated into the solution of the fundamental matrix to reduce the number of parameters to be solved. Subsequently, an inlier updating matrix was introduced according to the threshold of the epipolar geometry distance to eliminate the potential outliers and obtain a reliable initial value of the fundamental matrix. On this basis, we employed a four-point iterative method to estimate the fundamental matrix and make it satisfy the rank constraint at the same time. Finally, the epipolar geometry in binocular vision was extended to triple-view, and the fundamental matrix obtained in the previous step was globally optimized by minimizing the coordinate deviation between the intersection point and feature point in each group of images. The experiments show that the proposed fundamental matrix estimation method is robust to noise and outliers. In the attitude measurement, the maximum static error was 0.104° and dynamic measurement error was superior to 0.273°, which improved the reconstruction accuracy of feature points. Indoor images were further used to test the method, and the mean rotation angle error was 0.362°. The results demonstrate that the estimation method proposed in this paper has a good practical application prospect in multi-view 3D reconstruction and visual localization.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…The authors of [28,29] used different geometric constraints to iteratively solve the fundamental matrix. Moreover, Xiao et al [30] proposed a fundamental matrix estimation method based on inlier set sample optimization. By adopting guided sampling and local optimization, this method can effectively deal with outliers.…”

Section: Introductionmentioning

confidence: 99%

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Liang

Huo

2022

Sensors

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“…Correct matches tend to form clusters with specific motions, and previous works proposed explicit geometrical checks for guaranteeing a consistent transformation of the inlier point set [20,27,61], based on local planarity or local contour invariance. More recently, datadriven strategies for selecting consistent observations have been proposed; for example, in [71] the authors rely on a one-class SVM to select a reliable candidate inlier set, and in [28] a motion model based on bilateral functions is used. However, all these approaches which rely on higher-level perceptual information in order to validate the inlier set coherent motion are not effective in complex urban environments with scarce candidates, abrupt and frequent depth variations of the scene and inconsistent edge detections due to significant viewpoint changes (see, for example, Fig.…”

Section: Related Workmentioning

confidence: 99%

Wide baseline pose estimation from video with a density-based uncertainty model

Pellicanò

Aldea

Hégarat‐Mascle

2019

Machine Vision and Applications

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Robust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error.

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“…2, April 2022: 1429-1436 1430 the determination of the F matrix [13]. Among the techniques of the evaluation of the extracted points we find in the literature (LMedS) [14], random sample consensus (RANSAC) [15] and m-estimator [16]. In this paper we present a technique for determining the relationship between two images with different modifications in order to optimize the computation time and the projection error.…”

Section: Introductionmentioning

confidence: 99%

Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

Chater

Benradi

Lasfar

2022

IJECE

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<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>

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An efficient fundamental matrix estimation method for wide baseline images

Cited by 6 publications

References 36 publications

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

A Global Fundamental Matrix Estimation Method of Planar Motion Based on Inlier Updating

Wide baseline pose estimation from video with a density-based uncertainty model

Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

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