The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
LoFTR: Detector-Free Local Feature Matching with Transformers
Open accessAbstract
We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volume to search correspondences, we use self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images. The global receptive field provided by Transformer enables our method to produce dense matches in low-texture areas, where feature detectors usually struggle to produce repeatable interest points. The experiments on indoor and outdoor datasets show that LoFTR outperforms state-of-the-art methods by a large margin. LoFTR also ranks first on two public benchmarks of visual localization among the published methods. Code is available at our project page: https://zju3dv.github.io/loftr/.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Feature Pyramid Networks for Object Detection
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End-to-End Object Detection with Transformers
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A performance evaluation of local descriptors
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Cited by
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Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Wen, B., Yang, W., Kautz, J., & Birchfield, S. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01692 · Cited by 353
Grounding Image Matching in 3D with MASt3R
Leroy, V., Cabon, Y., & Revaud, J. (2024) · arXiv · doi:10.48550/arxiv.2406.09756 · Cited by 309
COTR: Correspondence Transformer for Matching Across Images
Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., & Yi, K. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00615 · Cited by 282
GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer
Qin, Z., Yu, H., Wang, C., Guo, Y., Peng, Y., Ilic, S., Hu, D., & Xu, K. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3259038 · Cited by 279
Metadata
- Authors
- Sun, J., Shen, Z., Wang, Y., Bao, H., & Zhou, X.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8918-8927
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3166285241
- Cited by
- 1,719
- References
- 55
- FWCI
- 78.49
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sun, J., Shen, Z., Wang, Y., Bao, H., & Zhou, X. (2021). LoFTR: Detector-Free Local Feature Matching with Transformers. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00881
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