Learning Affine Correspondences by Integrating Geometric Constraints

• URL: http://arxiv.org/abs/2504.04834v2
• Date: Thu, 10 Apr 2025 13:40:31 GMT
• Title: Learning Affine Correspondences by Integrating Geometric Constraints
• Authors: Pengju Sun, Banglei Guan, Zhenbao Yu, Yang Shang, Qifeng Yu, Daniel Barath,
• Abstract summary: We present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints.<n>Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator.<n>The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks.
• Score: 30.695253062973784
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Affine correspondences have received significant attention due to their benefits in tasks like image matching and pose estimation. Existing methods for extracting affine correspondences still have many limitations in terms of performance; thus, exploring a new paradigm is crucial. In this paper, we present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints. Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator. For this purpose, we propose loss functions based on geometric constraints, which can effectively improve accuracy by supervising neural networks to learn feature geometry. The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks. To further demonstrate the effectiveness of the proposed method, we applied it to relative pose estimation. Affine correspondences extracted by our method lead to more accurate poses than the baselines on a range of real-world datasets. The code is available at https://github.com/stilcrad/DenseAffine.

Related papers

• Robust Two-View Geometry Estimation with Implicit Differentiation [2.048226951354646]
  We present a novel two-view geometry estimation framework. It is based on a differentiable robust loss function fitting. We evaluate our approach on the camera pose estimation task in both outdoor and indoor scenarios.
  arXiv  Detail & Related papers  (2024-10-23T15:51:33Z)
• Geometry-aware Feature Matching for Large-Scale Structure from Motion [10.645087195983201]
  We introduce geometry cues in addition to color cues to fill gaps when there is less overlap in large-scale scenarios. Our approach ensures that the denser correspondences from detector-free methods are geometrically consistent and more accurate. It outperforms state-of-the-art feature matching methods on benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T21:41:35Z)
• Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature [81.25511385257344]
  We present a novel solution, Q-REG, which utilizes rich geometric information to estimate the rigid pose from a single correspondence. Q-REG allows to formalize the robust estimation as an exhaustive search, hence enabling end-to-end training. We demonstrate in the experiments that Q-REG is agnostic to the correspondence matching method and provides consistent improvement both when used only in inference and in end-to-end training.
  arXiv  Detail & Related papers  (2023-09-27T20:58:53Z)
• IMP: Iterative Matching and Pose Estimation with Adaptive Pooling [34.36397639248686]
  We propose an textbfefficient IMP, called EIMP, to dynamically discard keypoints without potential matches. Experiments on YFCC100m, Scannet, and Aachen Day-Night datasets demonstrate that the proposed method outperforms previous approaches in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-04-28T13:25:50Z)
• Spelunking the Deep: Guaranteed Queries for General Neural Implicit Surfaces [35.438964954948574]
  This work presents a new approach to perform queries directly on general neural implicit functions for a wide range of existing architectures. Our key tool is the application of range analysis to neural networks, using automatic arithmetic rules to bound the output of a network over a region. We use the resulting bounds to develop queries including ray casting, intersection testing, constructing spatial hierarchies, fast mesh extraction, closest-point evaluation.
  arXiv  Detail & Related papers  (2022-02-05T00:37:08Z)
• Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification [114.56752624945142]
  We argue that the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. We propose an efficient mini batch sampling method called Graph Sampling (GS) for large-scale metric learning.
  arXiv  Detail & Related papers  (2021-04-04T06:44:15Z)
• Deep Reinforcement Learning of Graph Matching [63.469961545293756]
  Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision. We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs. Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
  arXiv  Detail & Related papers  (2020-12-16T13:48:48Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• 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)
• Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning [86.45526827323954]
  Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. We propose an iterative algorithm to learn such pairwise relations. We show that the proposed algorithm performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-19T10:32:03Z)

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.