Two-View Geometry Estimation Using RANSAC With Locality Preserving Constraint

Gang, Wang; Sun, Xiaoliang; Shang, Yang; Wang, Zi; Shi, Zhongchen; Yu, Qifeng

doi:10.1109/access.2020.2964425

Cited by 15 publications

(15 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over time, different methods have been proposed to reject the false-positive correspondences and improve the RANSAC, such as GR_RANSAC (Elashry et al, 2021) which depends on the geometric relation between the features but requires adjustable thresholds based on the images' relative orientation, SuperGlue (Sarlin et al, 2020) which learns feature matching based on Graph Neural Networks but needs to learn each feature based on all the features in the same image and the other image, and thus, it consumes more time, and LP-RANSAC (Wang et al, 2020) which uses RANSAC with locality preserving constraint. The specific objective of this study is to propose a filtering algorithm based on the Graph Networks, as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers and needs no variable threshold or to learn features, etc.…”

Section: Figure 1 Ransac Familymentioning

confidence: 99%

Feature Matching Enhancement Using the Graph Neural Network (Gnn-Ransac)

Elashry

Toth

2022

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

View full text Add to dashboard Cite

Abstract. Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization and Mapping (VSLAM), etc., where the RANSAC algorithm is frequently used for outlier detection; note that RANSAC is the most widely used robust approach in photogrammetry and computer vision for outlier detection. It is known that the outlier ratio used in RANSAC primarily determines the number of trial runs needed, which eventually, determines the computation time. Over time, different methods have been proposed to reject the false-positive correspondences and improve RANSAC, such as GR_RANSAC, SuperGlue, and LPRANSAC. The specific objective of this study is to propose a filtering algorithm based on Graph Neural Networks (GNN), as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers. The research is based on the idea that descriptors of corresponding points, as well as their spatial relationship, should be similar in image sequences. In graph representation, built by the adjacency matrix of data (nodes features), there should be similarity for corresponding points that are close to each other in the image domain. From the many GNNs techniques, Graph Attention Networks (GATs) were selected for this study as they assign different importance to each neighbour’s contribution as anisotropic operations, so the features of neighbour nodes are not considered in the same way, unlike other GNNs techniques. In our approach, we build a graph in each image, because the similarity of the two-dimensional spatial relationships between points in the image domain of consecutive images should be similar. Then during processing, points with any significantly different neighbours are considered as outliers. Next, the points can be updated in the GNN layer. GNN-RANSAC is tested experimentally on real image pairs. Clearly, the proposed pre-filtering increases the inlier ratio and results in faster convergence compared to ordinary RANSAC, making it attractive for real-time applications. Furthermore, there is no need to learn the features.

show abstract

Section: Figure 1 Ransac Familymentioning

confidence: 99%

Feature Matching Enhancement Using the Graph Neural Network (Gnn-Ransac)

Elashry

Toth

2022

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

View full text Add to dashboard Cite

show abstract

“…Ma et al [10] proposed a locality preserving (LP) matching method to prune correspondences in C ini if they violate the spatial neighborhood relationship. Using Ma et al's method [10] as a preprocessing step to create a reduced correspondence set C reduced , Wang et al [11] randomly sampled four correspondences from C reduced , and then calculated the estimated model solution of C reduced . Further, using the estimated model solution, the HAV process is performed on C reduced to obtain the inlier rate of C reduced .…”

Section: Related Workmentioning

confidence: 99%

A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

2022

View full text Add to dashboard Cite

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods.

show abstract

“…In SVH-RANSAC [28], diverse scale constraints have been enforced, and underlying mismatches can be recognized by abnormal scale. LP-RANSAC [29] integrates local preserving constraint into the universal RANSAC framework, which prunes those unreliable correspondences before the hypothesize-andverify loop. Zheng [30] proposes an image registration method by using structured topological constraints, which establishes the correspondences of local points, triangular edges and triangular surfaces to dynamically erase mismatches.…”

Section: Related Workmentioning

confidence: 99%

Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

et al. 2021

View full text Add to dashboard Cite

Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in longdistance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This paper puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the reprojection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.

show abstract

Two-View Geometry Estimation Using RANSAC With Locality Preserving Constraint

Cited by 15 publications

References 40 publications

Feature Matching Enhancement Using the Graph Neural Network (Gnn-Ransac)

Feature Matching Enhancement Using the Graph Neural Network (Gnn-Ransac)

A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

Contact Info

Product

Resources

About