Image Matching Filtering and Refinement by Planes and Beyond

• URL: http://arxiv.org/abs/2411.09484v2
• Date: Fri, 15 Nov 2024 17:48:31 GMT
• Title: Image Matching Filtering and Refinement by Planes and Beyond
• Authors: Fabio Bellavia, Zhenjun Zhao, Luca Morelli, Fabio Remondino,
• Abstract summary: This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods.
• Score: 8.184339776177486
• License:
• Abstract: This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. Assuming that motion flow within the scene can be approximated by local homography transformations, matches are aggregated into overlapping clusters corresponding to virtual planes using an iterative RANSAC-based approach, with non-conforming correspondences discarded. Moreover, the underlying planar structural design provides an explicit map between local patches associated with the matches, enabling optional refinement of keypoint positions through cross-correlation template matching after patch reprojection. Finally, to enhance robustness and fault-tolerance against violations of the piece-wise planar approximation assumption, a further strategy is designed for minimizing relative patch distortion in the plane reprojection by introducing an intermediate homography that projects both patches into a common plane. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Unlike other current comparisons, the proposed benchmark also takes into account the more general, real, and practical cases where camera intrinsics are unavailable. Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods. Finally, this study suggests that there are still development potential in actual image matching solutions in the considered research direction, which could be in the future incorporated in novel deep image matching architectures.

Related papers

• APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging [74.9262846410559]
  Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging. Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images. We propose a self-supervised SVCT reconstruction method -- Anti-Aliasing Projection Representation Field (APRF) APRF can build the continuous representation between adjacent projection views via the spatial constraints.
  arXiv  Detail & Related papers  (2023-07-11T14:04:12Z)
• Image Matching by Bare Homography [9.431261418370147]
  This paper presents Slime, a novel non-deep image matching framework which models the scene as rough local overlapping planes. Planes are mutually extended by compatible matches and the images are split into fixed tiles, with only the best homographies retained for each pair of tiles. The paper gives a thorough comparative analysis of recent state-of-the-art in image matching represented by end-to-end deep networks and hybrid pipelines.
  arXiv  Detail & Related papers  (2023-05-15T18:35:47Z)
• Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal [17.90609572352273]
  This paper proposes an automatic image registration approach through line-support region segmentation and geometrical outlier removal (ALRS-GOR) It is designed to address the problems associated with the registration of images with affine deformations and inconsistent content. Various image sets have been considered for the evaluation of the proposed approach, including aerial images with simulated affine deformations.
  arXiv  Detail & Related papers  (2022-04-02T10:47:16Z)
• Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
  We propose a multi-level contrastive learning approach for semantic matching. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
  arXiv  Detail & Related papers  (2021-09-22T18:34:14Z)
• Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry [38.20137500372927]
  We propose a method consisting of two camera pose estimators that deal with the information from pairwise images. For image sequences, a Transformer-like structure is adopted to build a geometry model over a local temporal window. A Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit the relationship between pairwise images.
  arXiv  Detail & Related papers  (2020-12-08T19:39:26Z)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)
• Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints [80.60538408386016]
  Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry. We propose an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection.
  arXiv  Detail & Related papers  (2020-07-29T21:41:31Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours [12.791313859673187]
  "ProAlignNet" accounts for large scale misalignments and complex transformations between the contour shapes. It learns by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric. In two real-world applications, the proposed models consistently perform superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-23T14:56:14Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)
• Leveraging Photogrammetric Mesh Models for Aerial-Ground Feature Point Matching Toward Integrated 3D Reconstruction [19.551088857830944]
  Integration of aerial and ground images has been proved as an efficient approach to enhance the surface reconstruction in urban environments. Previous studies based on geometry-aware image rectification have alleviated this problem. We propose a novel approach: leveraging photogrammetric mesh models for aerial-ground image matching.
  arXiv  Detail & Related papers  (2020-02-21T01:47:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.