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Paper Citation Record · LEDGER

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives

As of 13 August 2026, this Paper Citation Record lists 100 of 194 outbound references and 0 inbound Pith citation observations for arXiv:2608.11093.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.11093 v1

Coverage vector

measured 100 of 194 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:29:54.583232Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 194 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved99
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c42ed459-c1fc-4b19-ae51-2fdf0dac50a1 · outbound

This paper cites Deep non-rigid structure from motion with missing data,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Deep non-rigid structure from motion with missing data,

Reference 1

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Observation 009ab42b-30fb-450f-9fa6-0aa1e847fa2a · outbound

This paper cites InLoc: Indoor visual localization with dense matching and view synthesis,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives InLoc: Indoor visual localization with dense matching and view synthesis,

Reference 2

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Observation 57a00785-7d51-4847-9456-722a4eb41ca5 · outbound

This paper cites Linear RGB-D SLAM for structured environments,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Linear RGB-D SLAM for structured environments,

Reference 3

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Observation 1e2bcdbe-47b2-4584-a407-a6bafb93e7d0 · outbound

This paper cites SuperGlue: Learning feature matching with graph neural networks,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives SuperGlue: Learning feature matching with graph neural networks,

Reference 4

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Observation 3a1981f8-158c-45bf-89b1-22908efd71d7 · outbound

This paper cites LoFTR: Detector-free local feature matching with transformers,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LoFTR: Detector-free local feature matching with transformers,

Reference 5

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Observation 1228c833-7a44-4c72-b0d9-589623350e06 · outbound

This paper cites Jamma: Ultra-lightweight local feature matching with joint mamba,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Jamma: Ultra-lightweight local feature matching with joint mamba,

Reference 6

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Observation 13dfc38d-a7e1-4c7f-869c-adf6213dc65a · outbound

This paper cites MambaGlue: Fast and robust local feature matching with mamba,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives MambaGlue: Fast and robust local feature matching with mamba,

Reference 7

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Observation 7aa018fc-f4fb-4d06-9097-6e9a3b4c2bb6 · outbound

This paper cites LightGlue: Local feature matching at light speed,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LightGlue: Local feature matching at light speed,

Reference 8

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source=pdf_text observed=2026-08-12T10:29:54.230301Z digest=sha256:f4ca0d95476b62ad8723d5ace628c04074c281e374f6dac6daaa97cdf04ca759

Observation 1028f353-df54-4915-8abf-64e06233961c · outbound

This paper cites OmniGlue: Generalizable feature matching with foundation model guidance,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives OmniGlue: Generalizable feature matching with foundation model guidance,

Reference 9

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source=pdf_text observed=2026-08-12T10:29:54.234036Z digest=sha256:62505f08bd065ce4df19404de4ab7b21637d0e728a00063a586ed836e734ce03

Observation 2e8ad8f9-fe33-4caf-b602-7ae7f63605f3 · outbound

This paper cites Roma: Robust dense feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Roma: Robust dense feature matching,

Reference 10

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source=pdf_text observed=2026-08-12T10:29:54.237713Z digest=sha256:bb96611cddd951bd8a133b77657fe2c47209d4a8b1158c3d2269fd3cf28321cb

Observation 45061f44-5862-4bd7-996d-6a3cc113d9d8 · outbound

This paper cites Grounding image matching in 3D with MASt3R,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Grounding image matching in 3D with MASt3R,

Reference 11

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Observation 91c1235c-be19-4848-962d-81da52add4be · outbound

This paper cites VGGT: Visual geometry grounded transformer,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives VGGT: Visual geometry grounded transformer,

Reference 12

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Observation 030f509b-72d9-48d6-a566-d91d753c1be2 · outbound

This paper cites Image matching from handcrafted to deep features: A survey,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Image matching from handcrafted to deep features: A survey,

Reference 13

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Observation db37cb3d-b444-4071-894d-dbf16f79638a · outbound

This paper cites A survey of feature matching methods,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives A survey of feature matching methods,

Reference 14

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source=pdf_text observed=2026-08-12T10:29:54.253502Z digest=sha256:026d2b06e4f2a9d640b90603599eb80626a43a969faaedb8b864bef9c05f80da

Observation 220f5a4c-358e-4e84-be1b-d5c560800330 · outbound

This paper cites A review of multimodal image matching: Methods and applications,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives A review of multimodal image matching: Methods and applications,

Reference 15

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Observation a9688bf8-5f9a-489b-96fd-1eb12507c55f · outbound

This paper cites A combined corner and edge detector,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives A combined corner and edge detector,

Reference 16

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source=pdf_text observed=2026-08-12T10:29:54.260611Z digest=sha256:c04497dc44088b6c1a46df3052d558ca6056924b117e979030d95324fa524ecc

Observation 50bd8727-55bc-436f-90cd-7af9e5e7a524 · outbound

This paper cites Distinctive image features from scale-invariant key- points,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Distinctive image features from scale-invariant key- points,

Reference 17

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Observation 7b8c09db-6faa-4b08-935c-31d62213a726 · outbound

This paper cites SURF: Speeded up robust features,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives SURF: Speeded up robust features,

Reference 18

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Observation b8776477-de37-4175-85d0-5a5159281b76 · outbound

This paper cites Robust wide-baseline stereo from maximally stable extremal regions,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Robust wide-baseline stereo from maximally stable extremal regions,

Reference 19

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Observation 164d7db5-0244-4011-9849-cf948d8dff5f · outbound

This paper cites Machine learning for high-speed corner detection,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Machine learning for high-speed corner detection,

Reference 20

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Observation 57f18509-8b21-45c6-b3d9-d2a19978031d · outbound

This paper cites Adaptive and generic corner detection based on the accelerated segment test,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Adaptive and generic corner detection based on the accelerated segment test,

Reference 21

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Observation 1faa76a9-1a23-41db-8bb4-3bab77ae407a · outbound

This paper cites KAZE features,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives KAZE features,

Reference 22

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Observation fb1a6afd-04d1-4b97-8ec0-bf3706d3cf99 · outbound

This paper cites Fast explicit diffusion for accelerated features in nonlinear scale spaces,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Fast explicit diffusion for accelerated features in nonlinear scale spaces,

Reference 23

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Observation 6b786e08-8590-4019-a17f-cd4641543b86 · outbound

This paper cites BRISK: Binary robust invariant scalable keypoints,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives BRISK: Binary robust invariant scalable keypoints,

Reference 24

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Observation fe3c829f-70dc-43b6-9747-31885cddaeb0 · outbound

This paper cites LIFT: Learned invariant feature transform,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LIFT: Learned invariant feature transform,

Reference 25

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Observation 0cc13644-329b-48cd-9194-3adc958bbada · outbound

This paper cites TILDE: A temporally invariant learned detector,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives TILDE: A temporally invariant learned detector,

Reference 26

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Observation 8516ef24-9664-40d6-862a-1a02cee8339c · outbound

This paper cites SuperPoint: Self- supervised interest point detection and description,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives SuperPoint: Self- supervised interest point detection and description,

Reference 27

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Observation bd861afc-b576-439b-befe-c32c216f7912 · outbound

This paper cites D2-Net: A trainable CNN for joint description and detection of local features,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives D2-Net: A trainable CNN for joint description and detection of local features,

Reference 28

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Observation 25e9d22d-69ac-4716-9953-a08de71d5264 · outbound

This paper cites R2D2: Reliable and repeatable detector and descriptor,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives R2D2: Reliable and repeatable detector and descriptor,

Reference 29

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Observation 8aef27b8-85d2-4a6a-b0cb-78302be304e2 · outbound

This paper cites Key.Net: Keypoint detection by handcrafted and learned CNN filters,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Key.Net: Keypoint detection by handcrafted and learned CNN filters,

Reference 30

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Observation d2549af7-6286-419b-9eb5-8be281575c99 · outbound

This paper cites Key.Net: Keypoint detection by handcrafted and learned CNN filters revisited,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Key.Net: Keypoint detection by handcrafted and learned CNN filters revisited,

Reference 31

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Observation caab876a-cfc5-4083-af33-1c60545128c4 · outbound

This paper cites ALIKE: Accurate and lightweight keypoint detection and descriptor extraction,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ALIKE: Accurate and lightweight keypoint detection and descriptor extraction,

Reference 32

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Observation 86549d0d-cdc3-43f1-9aa8-b46a2d0d5b85 · outbound

This paper cites Quad- Networks: Unsupervised learning to rank for interest point detection,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Quad- Networks: Unsupervised learning to rank for interest point detection,

Reference 33

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Observation 564c32f7-9f07-453c-85de-b08a14c9649d · outbound

This paper cites Disk: Learning local fea- tures with policy gradient,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Disk: Learning local fea- tures with policy gradient,

Reference 34

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source=pdf_text observed=2026-08-12T10:29:54.335723Z digest=sha256:4413e7a250618d8e55bd3b6b45f2e66043db956633aa8fab986faec4bf5dd06a

Observation a30033f3-7854-4bb1-bf89-1ba27ef1f504 · outbound

This paper cites Dedode v2: Analyzing and improving the dedode keypoint detector,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Dedode v2: Analyzing and improving the dedode keypoint detector,

Reference 35

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Observation 5e4442ff-e476-4435-a677-e9c452c0068b · outbound

This paper cites DaD: Distilled Reinforcement Learning for Diverse Keypoint Detection.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives DaD: Distilled Reinforcement Learning for Diverse Keypoint Detection

Reference 36

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Observation 073b3d11-8635-4739-a17b-ad066e88e57d · outbound

This paper cites PCA-SIFT: a more distinctive representation for local image descriptors,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives PCA-SIFT: a more distinctive representation for local image descriptors,

Reference 37

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Observation c7b6f2c1-5a96-4527-b10f-618cc532a69c · outbound

This paper cites A performance evaluation of local descriptors,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives A performance evaluation of local descriptors,

Reference 38

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Observation dcb8260e-dec5-4545-8a48-2ed0736a9f1f · outbound

This paper cites BRIEF: Binary robust independent elementary features,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives BRIEF: Binary robust independent elementary features,

Reference 39

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Observation 020054b6-dca8-4954-9dd1-b89e532b4aaa · outbound

This paper cites FREAK: Fast retina key- point,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives FREAK: Fast retina key- point,

Reference 40

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Observation 32ba067b-5d8d-4010-b48d-0e5e76d8199e · outbound

This paper cites Local intensity order pattern for feature description,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Local intensity order pattern for feature description,

Reference 41

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source=pdf_text observed=2026-08-12T10:29:54.362770Z digest=sha256:a7b8b1d84756f5ed149607f59066c87a8c40794553ade77c87983bb169e3dcc3

Observation 4bb60a09-558c-4f04-a8bb-4a6e15679ba8 · outbound

This paper cites Rethinking the sGLOH descriptor,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Rethinking the sGLOH descriptor,

Reference 42

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source=pdf_text observed=2026-08-12T10:29:54.366649Z digest=sha256:b5c5555374b38519a73f3b4d2ac751512e6059b24f1c57d750642aeedf2556f7

Observation 31cdc2e2-4404-43d1-9664-b23341ebdc1e · outbound

This paper cites Discriminative learning of deep convolutional feature point descriptors,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Discriminative learning of deep convolutional feature point descriptors,

Reference 43

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source=pdf_text observed=2026-08-12T10:29:54.370020Z digest=sha256:afa0c4d0b4994eacc8d48f14598880af3aecd46836518e6b00dee599da2eb174

Observation 0488c566-e872-4596-96c8-78173def4660 · outbound

This paper cites L2-Net: Deep learning of discrimina- tive patch descriptor in Euclidean space,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives L2-Net: Deep learning of discrimina- tive patch descriptor in Euclidean space,

Reference 44

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source=pdf_text observed=2026-08-12T10:29:54.373574Z digest=sha256:1d887d9d92b986789931a9f9a6880509ea2f7ed48adebbf95bcf24ff306da118

Observation 7ea363ec-d210-4bf8-9f6d-1b252614b670 · outbound

This paper cites Working hard to know your neighbor’s margins: local descriptor learning loss,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Working hard to know your neighbor’s margins: local descriptor learning loss,

Reference 45

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source=pdf_text observed=2026-08-12T10:29:54.376924Z digest=sha256:f4a38b0cccc799ead493bb4a2fb34bc72a1072801ab6456203e069521ce24e34

Observation 4fdaa36a-ca2c-4179-9c69-8ca9ac86ffee · outbound

This paper cites Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions,

Reference 46

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source=pdf_text observed=2026-08-12T10:29:54.380327Z digest=sha256:946fd7f50f3fe289fa6dc9c9013e69e20ef6881e31222cd35a517051f7b7a032

Observation e4e68600-37c7-4559-b403-e5cfed3dc34d · outbound

This paper cites GeoDesc: Learning local descriptors by integrating geometry constraints,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives GeoDesc: Learning local descriptors by integrating geometry constraints,

Reference 47

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source=pdf_text observed=2026-08-12T10:29:54.383743Z digest=sha256:39c8043e94b5cfada4949c857be543361e636aff834295e1b2a56f44a8955793

Observation c2edf3d3-99ad-4ec9-9fee-55e3cd8a25bc · outbound

This paper cites Local descriptors optimized for average precision,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Local descriptors optimized for average precision,

Reference 48

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source=pdf_text observed=2026-08-12T10:29:54.388090Z digest=sha256:e587813cfc4d710d92ff211f19c70aacb6a79a290dc67493e9b58e3e234b3415

Observation 9dcbd421-1cdc-44d9-8361-12016fee6040 · outbound

This paper cites ContextDesc: Local descriptor augmentation with cross- modality context,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ContextDesc: Local descriptor augmentation with cross- modality context,

Reference 49

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source=pdf_text observed=2026-08-12T10:29:54.391709Z digest=sha256:5612c832fd6b94f5d6e11cabfafdeb0462b4f04a1059944a44e1a9b67ce7c990

Observation c4dbc255-9745-4294-9b00-948b7100f85e · outbound

This paper cites HyNet: learning local descriptor with hybrid similarity measure and triplet loss,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives HyNet: learning local descriptor with hybrid similarity measure and triplet loss,

Reference 50

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source=pdf_text observed=2026-08-12T10:29:54.395519Z digest=sha256:098d7ebd8550212b1603e179c131feb9ebb8046bdafddae8c5b9e475d9c3e38f

Observation aacbf1e4-8375-4f60-b564-c7713653085d · outbound

This paper cites SOSNet: Second order similarity regularization for local descriptor learning,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives SOSNet: Second order similarity regularization for local descriptor learning,

Reference 51

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source=pdf_text observed=2026-08-12T10:29:54.399001Z digest=sha256:5a64834cbc01452d8d6a3c94dc3ddc7e59b498a4216a585efaf368af006fc67c

Observation 9ffa5e58-863d-4fd7-9c7c-cf73717fd05d · outbound

This paper cites Zippypoint: Fast interest point detection, description, and matching through mixed precision discretization,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Zippypoint: Fast interest point detection, description, and matching through mixed precision discretization,

Reference 52

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source=pdf_text observed=2026-08-12T10:29:54.402332Z digest=sha256:e0e80dad8b81fc7bad53ed56bfaad1aadf3ad6e7bb3f8c305028dc4c2f6dbde1

Observation 81087426-8822-40fa-bbf1-87f327ff44b1 · outbound

This paper cites Xfeat: Accelerated features for lightweight image matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Xfeat: Accelerated features for lightweight image matching,

Reference 53

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source=pdf_text observed=2026-08-12T10:29:54.406376Z digest=sha256:0e96936296e4f170d1dedff2f1135f8e3f89b7397efebba6011a1a4f97fdd9d6

Observation 47f614cd-169f-4722-83b7-cb3fd27edf64 · outbound

This paper cites NCTR: Neighborhood consensus transformer for feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives NCTR: Neighborhood consensus transformer for feature matching,

Reference 54

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source=pdf_text observed=2026-08-12T10:29:54.410316Z digest=sha256:d01988bf4b58386db720f81ac81e6174bb790b34e9170294690e07c224a5354a

Observation 6427698a-d457-4ad7-8a62-df02c24154fc · outbound

This paper cites DNC-Net: Dual-neighbourhood consensus net- work for feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives DNC-Net: Dual-neighbourhood consensus net- work for feature matching,

Reference 55

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source=pdf_text observed=2026-08-12T10:29:54.413691Z digest=sha256:1cf9ede0fb2fa3dde945b1eb2d141da92a3b13ab6c8e0ddd6bf2ee2d8ac7b438

Observation 79f27bae-0c6c-40c2-acac-165156940bd5 · outbound

This paper cites IMP: Iterative matching and pose estimation with adaptive pooling,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives IMP: Iterative matching and pose estimation with adaptive pooling,

Reference 56

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source=pdf_text observed=2026-08-12T10:29:54.417181Z digest=sha256:966c64fc2a75ed03c923fff9e5d7655f258ca10e1a601d80e636c01d0002a9fe

Observation 807e4619-4b43-4995-bd55-9ac3ffb3e58f · outbound

This paper cites Scene-aware feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Scene-aware feature matching,

Reference 57

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source=pdf_text observed=2026-08-12T10:29:54.420658Z digest=sha256:65b203fa4c6031a84db25b962c30d487809f7ba9af8266c94d0b12d30fe22d35

Observation bb3e1875-7637-4bb3-8a56-2901d79368d2 · outbound

This paper cites S2H- GNN: Learning soft to hard feature matching with sparsified graph neural network,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives S2H- GNN: Learning soft to hard feature matching with sparsified graph neural network,

Reference 58

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source=pdf_text observed=2026-08-12T10:29:54.425160Z digest=sha256:826948721e97ce4a74016497f3f22237e423bdc9d5e96f3ce34cc255f8f6d230

Observation a5abb442-63df-45e8-8426-0f007be69189 · outbound

This paper cites HTMatch: An efficient hybrid transformer based graph neural network for local feature match- ing,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives HTMatch: An efficient hybrid transformer based graph neural network for local feature match- ing,

Reference 59

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source=pdf_text observed=2026-08-12T10:29:54.428463Z digest=sha256:8aca86efb7f1ae1d9acb8c2f97ec0be490a8883747a231b2955adf1e40270539

Observation 3abca9ca-cfa1-464f-98d9-a0fc742fa914 · outbound

This paper cites DiffGlue: Diffusion-aided image feature match- ing,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives DiffGlue: Diffusion-aided image feature match- ing,

Reference 60

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source=pdf_text observed=2026-08-12T10:29:54.432076Z digest=sha256:41b52673207e491db9bbed0d98e878b47f2efcc7065115d7c692b70dd655ac3d

Observation 00574e76-f51d-4c30-b959-50f5071f87e5 · outbound

This paper cites ResMatch: Residual attention learning for feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ResMatch: Residual attention learning for feature matching,

Reference 61

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source=pdf_text observed=2026-08-12T10:29:54.435655Z digest=sha256:70b968811176e84911ffa3ccecdfb2c49cd0b1e8aeea244ae24aa523477b8cb9

Observation 0f35054d-3a5d-438e-81cb-c3d9a5d0b99c · outbound

This paper cites Matching while perceiving: Enhance image feature matching with applicable semantic amalgama- tion,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Matching while perceiving: Enhance image feature matching with applicable semantic amalgama- tion,

Reference 62

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source=pdf_text observed=2026-08-12T10:29:54.439663Z digest=sha256:35b54addd82e53f7f135b69270976ff801e27f2e6f0bfbbf3c32640bf2b95fc2

Observation e8ae62c5-d23b-4800-b484-773ddd8b6637 · outbound

This paper cites SemMatcher: Semantic- aware feature matching with neighborhood consensus,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives SemMatcher: Semantic- aware feature matching with neighborhood consensus,

Reference 63

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source=pdf_text observed=2026-08-12T10:29:54.442884Z digest=sha256:df3be0fc0edb42e6ceb42be57fca25f1a22f00b79dec1cbafc23fcdda2d27460

Observation 7a6cdd51-3a6a-4c3b-a2d2-4e51f55d6518 · outbound

This paper cites CoMatcher: Multi-view collaborative feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives CoMatcher: Multi-view collaborative feature matching,

Reference 64

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source=pdf_text observed=2026-08-12T10:29:54.446140Z digest=sha256:db1bfafcae76a4ef5bb6070617fed0577699b62f498a260cfc5e2df12c8ea858

Observation 07a1a1d2-8e5f-4247-9e0f-21c78eda1c49 · outbound

This paper cites AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching,

Reference 65

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source=pdf_text observed=2026-08-12T10:29:54.449686Z digest=sha256:bd266719c4f4b1692787968093228967b02ff3f9026f3ae52a6876beb58daf7c

Observation 8a8c43b9-172f-44ea-b626-820a7dadf6ae · outbound

This paper cites Learning to match features with seeded graph matching network,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Learning to match features with seeded graph matching network,

Reference 66

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source=pdf_text observed=2026-08-12T10:29:54.454542Z digest=sha256:60268710ba7ba7255455873ad93b3efc10f7e33853cb73fdc9fdaa96734adb03

Observation c39bbbac-a4ea-492e-bfb4-2fc3ff321cd6 · outbound

This paper cites Improving sparse graph attention for feature matching by informative keypoints exploration,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Improving sparse graph attention for feature matching by informative keypoints exploration,

Reference 67

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source=pdf_text observed=2026-08-12T10:29:54.458051Z digest=sha256:2fbe1c695c4210a42dd8e8e10f89ac0e6f1daebd967fa68078ec0dd730d49f4a

Observation fda0cdd9-7cce-466d-af26-79d140472bc8 · outbound

This paper cites ClusterGNN: Cluster-based coarse-to-fine graph neural network for efficient feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ClusterGNN: Cluster-based coarse-to-fine graph neural network for efficient feature matching,

Reference 68

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source=pdf_text observed=2026-08-12T10:29:54.461562Z digest=sha256:ee8f867d5bf0083eccd12893f85c712e67d81ffb39cb1fbb3f28da23f1aa03cd

Observation 611e3d88-b278-4a38-9914-88271e0042f9 · outbound

This paper cites Learning to match features with discriminative sparse graph neural network,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Learning to match features with discriminative sparse graph neural network,

Reference 69

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source=pdf_text observed=2026-08-12T10:29:54.465184Z digest=sha256:7196bd01acb4a6db8bd3eb4b7b83de2897e90508337b564b27c87ab060d9ca95

Observation adf08b25-89f7-4379-9bdc-985ceeca1e2e · outbound

This paper cites ParaFormer: Parallel attention transformer for efficient feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ParaFormer: Parallel attention transformer for efficient feature matching,

Reference 70

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source=pdf_text observed=2026-08-12T10:29:54.468596Z digest=sha256:bb79ae7bb87f10ef5e47927d6e7d46b1cbb784ce78a7772a0894eae1119d706c

Observation f04b8cbf-d546-46f1-a113-aaf20081fbc2 · outbound

This paper cites AMatFormer: Efficient feature matching via anchor matching transformer,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives AMatFormer: Efficient feature matching via anchor matching transformer,

Reference 71

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source=pdf_text observed=2026-08-12T10:29:54.472058Z digest=sha256:788a7a461f1621862464309e3ed720c2d73b4de6e9840f732b3efcdac52728e0

Observation 618c75b3-5774-4620-9305-ddf64ebec217 · outbound

This paper cites LightSGM: Local feature matching with lightweight seeded,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LightSGM: Local feature matching with lightweight seeded,

Reference 72

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source=pdf_text observed=2026-08-12T10:29:54.475478Z digest=sha256:feb94f0fac2923275faf40244175ab4af0a3e9ad78a99b288291dab51577819c

Observation dcd8494f-df14-45b3-bada-f6713c1ccb6c · outbound

This paper cites FilterGNN: Image feature matching with cascaded outlier filters and linear attention,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives FilterGNN: Image feature matching with cascaded outlier filters and linear attention,

Reference 73

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source=pdf_text observed=2026-08-12T10:29:54.478942Z digest=sha256:1ed0b0ba34eeb051e368aae8c6023041967f6d55d19b18b10d0eef8b49c3d5f0

Observation 800f5214-4e5e-4ade-a051-5bd28b200767 · outbound

This paper cites Learning feature matching via matchable keypoint- assisted graph neural network,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Learning feature matching via matchable keypoint- assisted graph neural network,

Reference 74

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source=pdf_text observed=2026-08-12T10:29:54.483359Z digest=sha256:fa964b749e0666e0657aabcf231a143210b5e4836624eb2e3f4f32912d5f5bb3

Observation fa72786e-f763-4b69-8055-cb701647adc2 · outbound

This paper cites Ada- Matcher: A deep detector-based local feature matcher with adaptive weight sharing,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Ada- Matcher: A deep detector-based local feature matcher with adaptive weight sharing,

Reference 75

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source=pdf_text observed=2026-08-12T10:29:54.486759Z digest=sha256:9420b0684bc41d08afa67644e2022339295db8c70b828d623d22c7968c42f2a9

Observation 8cb32076-1366-401f-bdca-93cb63fd53ab · outbound

This paper cites LFM-3D: Learnable feature matching across wide baselines using 3D signals,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LFM-3D: Learnable feature matching across wide baselines using 3D signals,

Reference 76

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source=pdf_text observed=2026-08-12T10:29:54.490050Z digest=sha256:188586f01e73db0b941ae3627a6bb2cfc93532e5f8407821645c225c6fd775b4

Observation 503feab4-33b6-4891-8098-42710ca76e0f · outbound

This paper cites MapGlue: Multimodal Remote Sensing Image Matching.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives MapGlue: Multimodal Remote Sensing Image Matching

Reference 77

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local_arxiv, observed 2026-08-12T10:29:55.363992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T10:29:54.493427Z digest=sha256:484fc4bf6347867146ab3e7733f7108958bf99a880d318ab715c2884c4cad074

Observation 56ffae71-53a8-41da-a718-19c2e56fd557 · outbound

This paper cites Patch2Pix: Epipolar-guided pixel-level correspondences,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Patch2Pix: Epipolar-guided pixel-level correspondences,

Reference 78

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source=pdf_text observed=2026-08-12T10:29:54.497383Z digest=sha256:66a727587785f72bbc4ca78e632e063b7416c80f704ebf9840cb2977e16642dc

Observation 269e1361-8e97-4a5e-b497-6f631177c3f0 · outbound

This paper cites Match- Former: Interleaving attention in transformers for feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Match- Former: Interleaving attention in transformers for feature matching,

Reference 79

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source=pdf_text observed=2026-08-12T10:29:54.500916Z digest=sha256:109f39af80a4efdc4b0475490a3ca8d047ef51548f1425401fd2689745a4cdb5

Observation e8919703-6429-4f47-a666-4dbbb5a9d723 · outbound

This paper cites ASpanFormer: Detector-free image matching with adaptive span transformer,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ASpanFormer: Detector-free image matching with adaptive span transformer,

Reference 80

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source=pdf_text observed=2026-08-12T10:29:54.504595Z digest=sha256:0173bf193e06b039ea344b96f6f5be29dfbbdaf808c39bccec78584ad0faeb01

Observation 98aedd7c-941b-4e3a-a2b5-a97ebdc8f07c · outbound

This paper cites 3DG-STFM: 3D geometric guided student-teacher feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives 3DG-STFM: 3D geometric guided student-teacher feature matching,

Reference 81

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source=pdf_text observed=2026-08-12T10:29:54.508000Z digest=sha256:e04f9b188b29bcac486d97f9dfcf48fd273d8631a0ed97fff6778da05c994b02

Observation 58d3fea5-4506-43f8-a3ac-f3861b867512 · outbound

This paper cites MSFormer: Multi-scale trans- former with neighborhood consensus for feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives MSFormer: Multi-scale trans- former with neighborhood consensus for feature matching,

Reference 82

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source=pdf_text observed=2026-08-12T10:29:54.511696Z digest=sha256:4e92ea5d934b34fb664ac96109532790e0f8f877cebe2a08b3cd63b72d315879

Observation 4c93144c-c2f9-4d70-b16c-8b51b765ec1c · outbound

This paper cites TopicFM: Robust and interpretable topic-assisted feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives TopicFM: Robust and interpretable topic-assisted feature matching,

Reference 83

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source=pdf_text observed=2026-08-12T10:29:54.515734Z digest=sha256:b139e8e1bcdd92be5f9f1f4ae7bd93c0c5fd41d9c2b4629c22d16023f0fbaf2f

Observation 5e3884d4-e12e-4ae6-b375-8e5bdcb578dd · outbound

This paper cites TopicFM+: Boosting accuracy and efficiency of topic-assisted feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives TopicFM+: Boosting accuracy and efficiency of topic-assisted feature matching,

Reference 84

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no resolver link, observed 2026-08-12T10:29:54.519844Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T10:29:54.519844Z digest=sha256:5039ffdb1e5337110ecf372eaa0afc28aefd2ce0763df825637a8e454b7d196d

Observation b10a1388-e22a-4f1b-9c21-b9637bbebb40 · outbound

This paper cites Adaptive spot- guided transformer for consistent local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Adaptive spot- guided transformer for consistent local feature matching,

Reference 85

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T10:29:54.524381Z digest=sha256:23f05b54bdc3fb9e337de19a424b1a4e6bdf14a95890bf119a2e58ef0e588a7f

Observation 9c2653d8-67bf-481e-b799-d3c069c96c32 · outbound

This paper cites Structured epipolar matcher for local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Structured epipolar matcher for local feature matching,

Reference 86

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source=pdf_text observed=2026-08-12T10:29:54.528165Z digest=sha256:9f3665f3993bed153ae99719df4630f7e1ab46df47a43cb5513778d4a88d5179

Observation d1931f02-ffa5-470b-ae79-bb75297caf11 · outbound

This paper cites Affine-based deformable attention and selective fusion for semi-dense matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Affine-based deformable attention and selective fusion for semi-dense matching,

Reference 87

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source=pdf_text observed=2026-08-12T10:29:54.532630Z digest=sha256:7d48ff699de0ec566a11a03f8544986ec3d662fbb24f7e2782b969ab01f9285b

Observation 8590e45f-7f3d-484a-9eec-ef0fea9b0d6e · outbound

This paper cites Beyond global cues: Unveiling the power of fine details in image matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives Beyond global cues: Unveiling the power of fine details in image matching,

Reference 88

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no resolver link, observed 2026-08-12T10:29:54.536788Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T10:29:54.536788Z digest=sha256:c81b80d815f7adedea94b259b8cf2f19dbe92bddc6854eba7f0a0fcefa687b17

Observation 6296731e-dd54-45d7-a9de-76469415c6de · outbound

This paper cites PRISM: Progressive dependency maximization for scale-invariant image matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives PRISM: Progressive dependency maximization for scale-invariant image matching,

Reference 89

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source=pdf_text observed=2026-08-12T10:29:54.540445Z digest=sha256:4a985d4e56585a6c7b96f5bf803247328581ac09f7453b436aaf00aa1f9a4166

Observation e752dd0c-ee79-4840-ae11-241957b2830d · outbound

This paper cites ContextMatcher: Detector-free feature matching with cross-modality context,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives ContextMatcher: Detector-free feature matching with cross-modality context,

Reference 90

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source=pdf_text observed=2026-08-12T10:29:54.544393Z digest=sha256:f606813623b82e3adabae7fa36a1c51a45588aa844007e702fb5add6e8596e03

Observation 4048810e-9e4a-49b5-b207-0edd491f8582 · outbound

This paper cites DeepMatcher: A deep transformer-based network for robust and accurate local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives DeepMatcher: A deep transformer-based network for robust and accurate local feature matching,

Reference 91

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source=pdf_text observed=2026-08-12T10:29:54.548097Z digest=sha256:b52cc8112b620d960b62ee5f24ef57275ae5aef4c2319952e6b54ceab169a629

Observation 879b567f-2bf0-4296-b634-1b17c377652d · outbound

This paper cites FMAP: Learning robust and accurate local feature matching with anchor points,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives FMAP: Learning robust and accurate local feature matching with anchor points,

Reference 92

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source=pdf_text observed=2026-08-12T10:29:54.551745Z digest=sha256:fce2012558b6cbc8c997d4d58d2c5c48c76246a56ea62c66c498c847fa4580e0

Observation 15db0028-95cc-4eca-8e1f-717d9fa0475b · outbound

This paper cites OAMatcher: An overlapping areas- based network with label credibility for robust and accurate feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives OAMatcher: An overlapping areas- based network with label credibility for robust and accurate feature matching,

Reference 93

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source=pdf_text observed=2026-08-12T10:29:54.555356Z digest=sha256:27fa57126ff85b0f2a68b7eae70bf1daa80bc52a4aed34479b1e81e1bfdb1056

Observation 41df6b0e-73cb-4967-b61a-096a1e27df45 · outbound

This paper cites DSAP: Dynamic sparse attention perception matcher for accurate local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives DSAP: Dynamic sparse attention perception matcher for accurate local feature matching,

Reference 94

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source=pdf_text observed=2026-08-12T10:29:54.558968Z digest=sha256:58283e1b5194c522a900baf73a9e563aa61f1abb082cacbcd287a1971ab44bc0

Observation c8078b41-05fc-4227-8ad5-00bc4fb35e9d · outbound

This paper cites CorMatcher: A corners-guided graph neural network for local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives CorMatcher: A corners-guided graph neural network for local feature matching,

Reference 95

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source=pdf_text observed=2026-08-12T10:29:54.563006Z digest=sha256:c6b1fddb35d506d7fe525bc76fef32b6e9d01c14fc0a151c2c6438e1b0cea754

Observation 141d889e-0312-44cb-a090-94238e62a7bc · outbound

This paper cites MR-Matcher: a multirouting transformer-based network for accurate local feature matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives MR-Matcher: a multirouting transformer-based network for accurate local feature matching,

Reference 96

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source=pdf_text observed=2026-08-12T10:29:54.566893Z digest=sha256:ad2fa782a8fe5c407e88f5f9b6d556ee71245a8340de88599672a393f707ea17

Observation 6b1cfd45-d3ad-4829-b765-2b2a0b8f3028 · outbound

This paper cites FMRT: Learning accurate feature matching with reconciliatory transformer,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives FMRT: Learning accurate feature matching with reconciliatory transformer,

Reference 97

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source=pdf_text observed=2026-08-12T10:29:54.570853Z digest=sha256:401a988b4337cafd07e1dd944d47721e11a7e8e16c988a5f2b1794086990a0b5

Observation d74c2b1a-6850-4ed6-88ec-14c44e0d2ed9 · outbound

This paper cites WinMRSI: Feature matching with window attention for multi-modal remote sensing image,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives WinMRSI: Feature matching with window attention for multi-modal remote sensing image,

Reference 98

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source=pdf_text observed=2026-08-12T10:29:54.575207Z digest=sha256:be769549f0ba5af4435bcdf26cb58883c95c13d5c54709d6ac7c89cf6cbc66ea

Observation db11bbe4-bbc6-46d5-9fc3-fde3511f4bab · outbound

This paper cites LGFCTR: Local and global feature convolu- tional transformer for image matching,.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives LGFCTR: Local and global feature convolu- tional transformer for image matching,

Reference 99

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source=pdf_text observed=2026-08-12T10:29:54.579197Z digest=sha256:d9d67c3dfa0a26a8442118e170261a3224cac3984377d8a5cf8038085f57232c

Observation 5dc4dcc3-5f43-493a-bb80-0cea3c01ff58 · outbound

This paper cites CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching.

Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching

Reference 100

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source=pdf_text observed=2026-08-12T10:29:54.583232Z digest=sha256:f86f7974de862469a3470e1346203844b858212438357460fc6a8241edae2d1e

Pith citing papers

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