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There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce Degree, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. Degree can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that Degree alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, Degree also reports competitive performance in the third Image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences. [ABSTRACT FROM AUTHOR]

This paper proposes an effective method, termed as motion-consistency driven matching (MCDM), for mismatch removal from given tentative correspondences between two feature sets. In particular, we regard each correspondence as a hypothetical node, and formulate the matching problem into a probabilistic graphical model to infer the state of each node (e.g., true or false correspondence). By investigating the motion consistency of true correspondences, a general prior is incorporated into our formulation to differentiate false correspondences from the true ones. The final inference is casted into an integer quadratic programming problem, and the solution is obtained by using an efficient optimization technique based on the Frank-Wolfe algorithm. Extensive experiments on general feature matching, as well as fundamental matrix estimation, relative pose estimation and loop-closure detection, demonstrate that our MCDM possesses strong generalization ability as well as high accuracy, which outperforms state-of-the-art methods. Meanwhile, due to the low computational complexity, the proposed method is efficient for practical feature matching tasks. [ABSTRACT FROM AUTHOR]

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields. [ABSTRACT FROM AUTHOR]

Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based implementation for fast calculation. GMS is robust to various challenging image changes, involving in viewpoint, scale, and rotation. It is also fast, e.g., take only 1 or 2 ms in a single CPU thread, even when 50K correspondences are processed. This has important implications for real-time applications. What's more, we show that incorporating GMS into the classic feature matching and epipolar geometry estimation pipeline can significantly boost the overall performance. Finally, we integrate GMS into the well-known ORB-SLAM system for monocular initialization, resulting in a significant improvement. [ABSTRACT FROM AUTHOR]

Feature matching problem that incorporates pair-wise constraints can be formulated as an Integer Quadratic Programming (IQP) problem with one-to-one matching constraint. Since it is NP-hard, relaxation models are required. One main challenge for optimizing IQP matching is how to incorporate the discrete one-to-one matching constraint in IQP matching optimization. In this paper, we present a new feature matching relaxation model, called Nonnegative Orthogonal Relaxation (NOR), that aims to optimize IQP matching problem in nonnegative orthogonal domain. One important benefit of the proposed NOR model is that it can naturally incorporate the discrete one-to-one matching constraint in its optimization and can return a desired sparse (approximate discrete) solution for the problem. An efficient and effective update algorithm has been developed to solve the proposed NOR model. Promising experimental results on several benchmark datasets demonstrate the effectiveness and efficiency of the proposed NOR method. [ABSTRACT FROM AUTHOR]

Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude. [ABSTRACT FROM AUTHOR]

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: local reference frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100, 98.9, 95.4 and 96.0 % respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets. [ABSTRACT FROM AUTHOR]

With recent advances in real-time implementations of filters for solving the simultaneous localization and mapping (SLAM) problem in the range-sensing domain, attention has shifted to implementing SLAM solutions using vision-based sensing. This paper presents and analyses different models of the Rao-Blackwellised particle filter (RBPF) for vision-based SLAM within a comprehensive application architecture. The main contributions of our work are the introduction of a new robot motion model utilizing structure from motion (SFM) methods and a novel mixture proposal distribution that combines local and global pose estimation. In addition, we compare these under a wide variety of operating modalities, including monocular sensing and the standard odometry-based methods. We also present a detailed study of the RBPF for SLAM, addressing issues in achieving real-time, robust and numerically reliable filter behavior. Finally, we present experimental results illustrating the improved accuracy of our proposed models and the efficiency and scalability of our implementation. [ABSTRACT FROM AUTHOR]

Automatic registration of range images is a fundamental problem in 3D modeling of free-from objects. Various feature matching algorithms have been proposed for this purpose. However, these algorithms suffer from various limitations mainly related to their applicability, efficiency, robustness to resolution, and the discriminating capability of the used feature representation. We present a novel feature matching algorithm for automatic pairwise registration of range images which overcomes these limitations. Our algorithm uses a novel tensor representation which represents semi-local 3D surface patches of a range image by third order tensors. Multiple tensors are used to represent each range image. Tensors of two range images are matched to identify correspondences between them. Correspondences are verified and then used for pairwise registration of the range images. Experimental results show that our algorithm is accurate and efficient. Moreover, it is robust to the resolution of the range images, the number of tensors per view, the required amount of overlap, and noise. Comparisons with the spin image representation revealed that our representation has more discriminating capabilities and performs better at a low resolution of the range images. [ABSTRACT FROM AUTHOR]

Popular algorithms for feature matching and model extraction fall into two broad categories: generate-and-test and Hough transform variations. However, both methods suffer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated model fit is poor due to error in the data used to generate the model position. Hough transform variations are less sensitive to noise, but implementations for complex problems suffer from large time and space requirements and from the detection of false positives. This paper describes a general method for solving problems where a model is extracted from, or fit to, data that draws benefits from both generate-and-test methods and those based on the Hough transform, yielding a method superior to both. An important component of the method is the subdivision of the problem into many subproblems. This allows efficient generate-and-test techniques to be used, including the use of randomization to limit the number of subproblems that must be examined. Each subproblem is solved using pose space analysis techniques similar to the Hough transform, which lowers the sensitivity of the method to noise. This strategy is easy to implement and results in practical algorithms that are efficient and robust. We describe case studies of the application of this method to object recognition, geometric primitive extraction, robust regression, and motion segmentation. [ABSTRACT FROM AUTHOR]