D2D: Keypoint Extraction with Describe to Detect Approach

• URL: http://arxiv.org/abs/2005.13605v1
• Date: Wed, 27 May 2020 19:27:46 GMT
• Title: D2D: Keypoint Extraction with Describe to Detect Approach
• Authors: Yurun Tian, Vassileios Balntas, Tony Ng, Axel Barroso-Laguna, Yiannis Demiris, Krystian Mikolajczyk
• Abstract summary: We present a novel approach that exploits the information within the descriptor space to propose keypoint locations. We propose an approach that inverts this process by first describing and then detecting the keypoint locations.
• Score: 48.0325745125635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations. Detect then describe, or detect and describe jointly are two typical strategies for extracting local descriptors. In contrast, we propose an approach that inverts this process by first describing and then detecting the keypoint locations. % Describe-to-Detect (D2D) leverages successful descriptor models without the need for any additional training. Our method selects keypoints as salient locations with high information content which is defined by the descriptors rather than some independent operators. We perform experiments on multiple benchmarks including image matching, camera localisation, and 3D reconstruction. The results indicate that our method improves the matching performance of various descriptors and that it generalises across methods and tasks.

Related papers

• FUSELOC: Fusing Global and Local Descriptors to Disambiguate 2D-3D Matching in Visual Localization [57.59857784298536]
  Direct 2D-3D matching algorithms require significantly less memory but suffer from lower accuracy due to the larger and more ambiguous search space. We address this ambiguity by fusing local and global descriptors using a weighted average operator within a 2D-3D search framework. We consistently improve the accuracy over local-only systems and achieve performance close to hierarchical methods while halving memory requirements.
  arXiv  Detail & Related papers  (2024-08-21T23:42:16Z)
• Improving the matching of deformable objects by learning to detect keypoints [6.4587163310833855]
  We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. We train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method. We also apply our method on the complex real-world task object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task.
  arXiv  Detail & Related papers  (2023-09-01T13:02:19Z)
• DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching [14.837075102089]
  Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Previous learning-based methods typically learn descriptors with keypoints, and treat the keypoint detection as a binary classification task on mutual nearest neighbours. In this work, we learn keypoints directly from 3D consistency. To this end, we derive a semi-supervised two-view detection objective to expand this set to a desired number of detections. Results show that our approach, DeDoDe, achieves significant gains on multiple geometry benchmarks.
  arXiv  Detail & Related papers  (2023-08-16T16:37:02Z)
• Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors [101.62384271200169]
  A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks. We investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval.
  arXiv  Detail & Related papers  (2022-09-27T17:59:04Z)
• P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching [78.18641868402901]
  This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. An ultra-wide reception mechanism in combination with a novel loss function are designed to mitigate the intrinsic information variations between pixel and point local regions.
  arXiv  Detail & Related papers  (2021-03-01T14:59:40Z)
• DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization [56.15308829924527]
  We propose a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points. For detecting 3D keypoints we predict the discriminativeness of the local descriptors in an unsupervised manner. Experiments on various benchmarks demonstrate that our method achieves competitive results for both global point cloud retrieval and local point cloud registration.
  arXiv  Detail & Related papers  (2020-07-17T20:21:22Z)
• Unconstrained Matching of 2D and 3D Descriptors for 6-DOF Pose Estimation [44.66818851668686]
  We generate a dataset of matching 2D and 3D points and their corresponding feature descriptors. To localize the pose of an image at test time, we extract keypoints and feature descriptors from the query image. The locations of the matched features are used in a robust pose estimation algorithm to predict the location and orientation of the query image.
  arXiv  Detail & Related papers  (2020-05-29T11:17:32Z)
• HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning [24.13425816781179]
  Local feature extraction remains an active research area due to the advances in fields such as SLAM, 3D reconstructions, or AR applications. We propose a method that treats both extractions independently and focuses on their interaction in the learning process. We show improvements over the state of the art in terms of image matching on HPatches and 3D reconstruction quality while keeping on par on camera localisation tasks.
  arXiv  Detail & Related papers  (2020-05-12T13:55:04Z)
• Learning and Matching Multi-View Descriptors for Registration of Point Clouds [48.25586496457587]
  We first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets.
  arXiv  Detail & Related papers  (2018-07-16T01:58:27Z)

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.