Results 31 to 40 of about 3,909,906 (397)
Learning Geometric Feature Embedding with Transformers for Image Matching
Sensors, 2022 Local feature matching is a part of many large vision tasks. Local feature matching usually consists of three parts: feature detection, description, and matching.
Xiaohu Nan, Lei Ding
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2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes.
Lu, Xiaoyong +3 more
openaire +2 more sources
Learning to Match Features with Seeded Graph Matching Network [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Accepted by ICCV2021, code to be realeased at https://github.com/vdvchen ...
Chen, Hongkai +7 more
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Guide Local Feature Matching by Overlap Estimation [PDF]
AAAI Conference on Artificial Intelligence, 2022 Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic consistency checks to guarantee ...
Ying Chen +4 more
semanticscholar +1 more source
AdaSG: A Lightweight Feature Point Matching Method Using Adaptive Descriptor with GNN for VSLAM
Sensors, 2022 Feature point matching is a key component in visual simultaneous localization and mapping (VSLAM). Recently, the neural network has been employed in the feature point matching to improve matching performance.
Ye Liu +6 more
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Learning to Guide Local Feature Matches [PDF]
2020 International Conference on 3D Vision (3DV), 2020 We tackle the problem of finding accurate and robust keypoint correspondences between images. We propose a learning-based approach to guide local feature matches via a learned approximate image matching. Our approach can boost the results of SIFT to a level similar to state-of-the-art deep descriptors, such as Superpoint, ContextDesc, or D2-Net and can
Darmon, François +2 more
openaire +6 more sources
3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching [PDF]
European Conference on Computer Vision, 2022 We tackle the essential task of finding dense visual correspondences between a pair of images. This is a challenging problem due to various factors such as poor texture, repetitive patterns, illumination variation, and motion blur in practical scenarios.
Runyu Mao +4 more
semanticscholar +1 more source
Horticultural Image Feature Matching Algorithm Based on Improved ORB and LK Optical Flow
Remote Sensing, 2022 To solve the low accuracy of image feature matching in horticultural robot visual navigation, an innovative and effective image feature matching algorithm was proposed combining the improved Oriented FAST and Rotated BRIEF (ORB) and Lucas–Kanade (LK ...
Qinhan Chen +6 more
doaj +1 more source
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting [PDF]
, 2020 For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios.
DG Lowe +6 more
core +3 more sources
Robust Feature Matching with Spatial Smoothness Constraints
Remote Sensing, 2020 Feature matching is to detect and match corresponding feature points in stereo pairs, which is one of the key techniques in accurate camera orientations.
Xu Huang, Xue Wan, Daifeng Peng
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