DFNet: Enhance Absolute Pose Regression with Direct Feature Matching [PDF]
European Conference on Computer Vision, 2022 We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. By incorporating exposure-adaptive novel view synthesis, our method successfully addresses photometric distortions in outdoor ...
Shuai Chen +3 more
semanticscholar +1 more source
FEATURE ANALYSIS FOR MULTI-WINDOW MATCHING [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Image matching is a practical way to build up the relationship between point pairs. In remote sensing and photogrammetry applications, area-based matching algorithms are usually used under control environment, such as fixing exterior orientation ...
W. C. Chang, L. C. Chen
doaj +1 more source
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2022 Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features.
Yanxing Shi +5 more
semanticscholar +1 more source
DeDoDe: Detect, Don’t Describe — Describe, Don’t Detect for Local Feature Matching [PDF]
International Conference on 3D Vision, 2023 Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene.
Johan Edstedt +3 more
semanticscholar +1 more source
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
doaj +1 more source
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 ...
Hongkai Chen +7 more
openaire +2 more sources
ParaFormer: Parallel Attention Transformer for Efficient Feature Matching [PDF]
AAAI Conference on Artificial Intelligence, 2023 Heavy computation is a bottleneck limiting deep-learning-based feature matching algorithms to be applied in many real-time applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks ...
Xiaoyong Lu +3 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
doaj +1 more source
TopicFM: Robust and Interpretable Topic-Assisted Feature Matching [PDF]
AAAI Conference on Artificial Intelligence, 2022 This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph ...
Khang Truong Giang +2 more
semanticscholar +1 more source
Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying [PDF]
, 2012 Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a ...
D. Wilson +3 more
core +1 more source