PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models | Paper Citation Record

Observation 8e08957f-df30-4a39-b100-e668b58de074 · outbound

This paper cites Unifying flow, stereo and depth estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Unifying flow, stereo and depth estimation,

Reference 1

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Observation a4856018-3235-4066-a28e-a20c3cf4d745 · outbound

This paper cites RGM: A Robust Generalizable Matching Model.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models RGM: A Robust Generalizable Matching Model

Reference 2

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Observation 22dc7895-08a4-4bef-9892-a0b22db49a05 · outbound

This paper cites VGGT: Visual geometry grounded transformer,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models VGGT: Visual geometry grounded transformer,

Reference 3

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Observation a68af55c-c400-42f4-938e-496ddc470e5d · outbound

This paper cites ITSA: An information-theoretic approach to au- tomatic shortcut avoidance and domain generalization in stereo matching networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models ITSA: An information-theoretic approach to au- tomatic shortcut avoidance and domain generalization in stereo matching networks,

Reference 4

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Observation dc776604-6663-4346-94a0-6840b3c8d2d5 · outbound

This paper cites Practical stereo matching via cascaded recurrent network with adaptive correlation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Practical stereo matching via cascaded recurrent network with adaptive correlation,

Reference 5

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Observation a84b3da9-beff-4998-b935-8ba936c4be83 · outbound

This paper cites Falling Things: A synthetic dataset for 3D object detection and pose estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Falling Things: A synthetic dataset for 3D object detection and pose estimation,

Reference 6

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Observation 8eb9d2df-ef9b-402f-aaef-51c33f30c590 · outbound

This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,

Reference 7

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Observation 523e6c0e-c3e1-425e-85cb-a6594a0f432f · outbound

This paper cites FlowNet: Learning optical flow with convolutional networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models FlowNet: Learning optical flow with convolutional networks,

Reference 8

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Observation 56bf137e-42f2-43c7-807b-9142825cefc0 · outbound

This paper cites AutoFlow: Learning a better training set for optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models AutoFlow: Learning a better training set for optical flow,

Reference 9

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Observation 3563ed27-7027-4748-a5c5-dca5bf7b8290 · outbound

This paper cites Virtual KITTI 2,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Virtual KITTI 2,

Reference 10

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Observation 623ac377-7737-4e01-99df-196e8ec5d2c1 · outbound

This paper cites Tartanair: A dataset to push the limits of visual SLAM,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Tartanair: A dataset to push the limits of visual SLAM,

Reference 11

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Observation d7227d8c-2405-4d4b-a851-34f393eddd60 · outbound

This paper cites Hypersim: A photore- alistic synthetic dataset for holistic indoor scene understanding,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Hypersim: A photore- alistic synthetic dataset for holistic indoor scene understanding,

Reference 12

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Observation 13a5178e-2507-4b54-9066-6fd6246ab2ff · outbound

This paper cites MegaDepth: Learning single-view depth prediction from internet photos,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models MegaDepth: Learning single-view depth prediction from internet photos,

Reference 13

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Observation 08a5d74e-5d15-4d08-b83d-1addc6550e21 · outbound

This paper cites CroCo v2: Improved cross-view completion pre-training for stereo match- ing and optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models CroCo v2: Improved cross-view completion pre-training for stereo match- ing and optical flow,

Reference 14

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Observation de3a09c5-6ba9-44da-9aff-9b1cd063a342 · outbound

This paper cites Stereo Anything: Unifying stereo matching with large-scale mixed data,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Stereo Anything: Unifying stereo matching with large-scale mixed data,

Reference 15

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Observation 74b6f3d5-a437-4759-98f7-f8da660c5c0a · outbound

This paper cites FoundationStereo: Zero-shot stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models FoundationStereo: Zero-shot stereo matching,

Reference 16

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Observation e0a1d5ed-4c20-4868-baf4-780e8c467411 · outbound

This paper cites Learning representations from foundation models for domain generalized stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Learning representations from foundation models for domain generalized stereo matching,

Reference 17

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Observation d86f6b7d-4b5f-4d06-a933-fcc639613a2f · outbound

This paper cites On the synergies between machine learning and binocular stereo for depth estimation from images: A survey,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models On the synergies between machine learning and binocular stereo for depth estimation from images: A survey,

Reference 18

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Observation 6de3392b-d0a8-49b9-906b-359a829e600c · outbound

This paper cites A survey on deep learning techniques for stereo-based depth estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A survey on deep learning techniques for stereo-based depth estimation,

Reference 19

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Observation 7a0af0fa-e914-4443-b8b6-bae8ea49fa71 · outbound

This paper cites Computing the stereo matching cost with a convolutional neural network,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Computing the stereo matching cost with a convolutional neural network,

Reference 20

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Observation f51a7b76-4f78-4d9e-9d0a-f9f75a090f57 · outbound

This paper cites SGM-Nets: Semi-global matching with neural networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models SGM-Nets: Semi-global matching with neural networks,

Reference 21

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Observation c5afac59-5f1e-4738-b339-0b533d4618e3 · outbound

This paper cites A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,

Reference 22

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Observation ab66bdd6-e65d-4876-9a7b-2df83edefeaa · outbound

This paper cites Accurate and efficient stereo processing by semi-global matching and mutual information,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Accurate and efficient stereo processing by semi-global matching and mutual information,

Reference 23

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Observation d6dc37c7-540a-41e5-8916-411c3cf9a9f5 · outbound

This paper cites Pyramid stereo matching network,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Pyramid stereo matching network,

Reference 24

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Observation 10d4a89f-97bf-4145-a64a-726100d771a6 · outbound

This paper cites GA- Net: Guided aggregation net for end-to-end stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models GA- Net: Guided aggregation net for end-to-end stereo matching,

Reference 25

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Observation 56802dd9-6455-4431-a8f2-2fdabac496a6 · outbound

This paper cites AANet: Adaptive aggregation network for efficient stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models AANet: Adaptive aggregation network for efficient stereo matching,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:58.567188Z

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Observation d2d53f5a-b8dd-4523-8e44-1bbe7aa29448 · outbound

This paper cites Attention concatenation volume for accurate and efficient stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Attention concatenation volume for accurate and efficient stereo matching,

Reference 27

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Observation abf963bc-a1cf-4617-9e5e-dec46f7c8369 · outbound

This paper cites Stereo matching using multi-level cost volume and multi-scale feature constancy,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Stereo matching using multi-level cost volume and multi-scale feature constancy,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:58.838315Z

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Observation c898ba81-bbe2-44e8-bcc7-1a63c5d605c6 · outbound

This paper cites Hierarchical deep stereo matching on high-resolution images,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Hierarchical deep stereo matching on high-resolution images,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:58.915961Z

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Observation d5a1e05f-e9bf-4754-b727-05ba1c421ffc · outbound

This paper cites Accurate and efficient stereo matching via attention concatenation volume,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Accurate and efficient stereo matching via attention concatenation volume,

Reference 30

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Observation de7dc1dc-b6e0-4247-b5bc-bbe80a89a97a · outbound

This paper cites HITNet: Hierarchical iterative tile refinement network for real-time stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models HITNet: Hierarchical iterative tile refinement network for real-time stereo matching,

Reference 31

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Observation f4e53f92-e248-463a-a7b0-b9793de1798a · outbound

This paper cites IGEV++: Iterative multi-range geometry encoding volumes for stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models IGEV++: Iterative multi-range geometry encoding volumes for stereo matching,

Reference 32

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Observation 4ee729f9-a356-4ed1-ab79-58ca44112ccb · outbound

This paper cites RAFT: recurrent all-pairs field transforms for optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models RAFT: recurrent all-pairs field transforms for optical flow,

Reference 33

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Observation 93f8cf5f-bb72-41de-818e-352348c577f1 · outbound

This paper cites RAFT-Stereo: Multilevel recur- rent field transforms for stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models RAFT-Stereo: Multilevel recur- rent field transforms for stereo matching,

Reference 34

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Observation f7d00240-a3fa-4288-b773-9941718b1a21 · outbound

This paper cites Iterative geometry encoding volume for stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Iterative geometry encoding volume for stereo matching,

Reference 35

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Observation 423a0676-0577-45ea-8189-9cb7b29695a0 · outbound

This paper cites Selective-Stereo: Adaptive frequency information selection for stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Selective-Stereo: Adaptive frequency information selection for stereo matching,

Reference 36

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Observation da4e5a55-8a53-4db5-8a93-b2f359dcee8c · outbound

This paper cites Domain-invariant stereo matching networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Domain-invariant stereo matching networks,

Reference 37

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Observation 470f34df-99ff-46f7-94e4-7ce5bb6d641f · outbound

This paper cites Matching-space stereo networks for cross-domain generalization,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Matching-space stereo networks for cross-domain generalization,

Reference 38

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Observation 14ea3598-6a0b-415f-9a1a-361604af975b · outbound

This paper cites GraftNet: Towards domain general- ized stereo matching with a broad-spectrum and task-oriented feature,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models GraftNet: Towards domain general- ized stereo matching with a broad-spectrum and task-oriented feature,

Reference 39

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Observation 3d7beaef-a09f-4870-848f-78268e2199d6 · outbound

This paper cites Revisiting domain generalized stereo matching networks from a feature consistency perspective,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Revisiting domain generalized stereo matching networks from a feature consistency perspective,

Reference 40

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Observation 6cb79a5a-77e7-45e0-a362-528734f59f09 · outbound

This paper cites An information-theoretic method to auto- matic shortcut avoidance and domain generalization for dense prediction tasks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models An information-theoretic method to auto- matic shortcut avoidance and domain generalization for dense prediction tasks,

Reference 41

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Observation a022320b-c9b7-4e48-acd1-1a1f855e8edb · outbound

This paper cites Domain generalized stereo matching via hierarchical visual transformation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Domain generalized stereo matching via hierarchical visual transformation,

Reference 42

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Observation 3c3ee99a-78e3-4d27-9fb9-19f662a7b9ac · outbound

This paper cites Masked representation learning for domain generalized stereo matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Masked representation learning for domain generalized stereo matching,

Reference 43

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Observation 15a37b8b-ac28-4807-9db4-8a5bf90e74e1 · outbound

This paper cites ProbFlow: Joint optical flow and uncertainty estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models ProbFlow: Joint optical flow and uncertainty estimation,

Reference 44

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Observation 70b19502-fd2d-409a-b9ef-23771ddac057 · outbound

This paper cites PWC-Net: Cnns for optical flow using pyramid, warping, and cost volume,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models PWC-Net: Cnns for optical flow using pyramid, warping, and cost volume,

Reference 45

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Observation 7b53521b-093e-4a8b-813e-69c5f4075f1c · outbound

This paper cites Flownet 2.0: Evolution of optical flow estimation with deep networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Flownet 2.0: Evolution of optical flow estimation with deep networks,

Reference 46

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Observation 3f49da2c-cb40-46d1-8459-a619b9fa4cf5 · outbound

This paper cites Learning to estimate hidden motions with global motion aggregation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Learning to estimate hidden motions with global motion aggregation,

Reference 47

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Observation 76c06f85-8484-4679-9a66-35ce32db27ae · outbound

This paper cites SEA-RAFT: simple, efficient, accurate RAFT for optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models SEA-RAFT: simple, efficient, accurate RAFT for optical flow,

Reference 48

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Observation 225ebbe0-b5c2-470f-96df-0f0415d8d4ac · outbound

This paper cites GMFlow: Learning optical flow via global matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models GMFlow: Learning optical flow via global matching,

Reference 49

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Observation dd342ed8-7abb-4622-b4f6-52b63b398424 · outbound

This paper cites FlowFormer: A transformer architecture for optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models FlowFormer: A transformer architecture for optical flow,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.751102Z

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Observation ab6f01c9-215e-4928-b5ed-d78e60f28b2d · outbound

This paper cites A naturalistic open source movie for optical flow evaluation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A naturalistic open source movie for optical flow evaluation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.737166Z

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Observation 342e49b3-281f-4111-bb8d-491753d34387 · outbound

This paper cites Object scene flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Object scene flow,

Reference 52

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Observation 4e1c594c-2e78-4345-b300-9c34ebcca8c3 · outbound

This paper cites Playing for bench- marks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Playing for bench- marks,

Reference 53

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Observation 8c8e03a1-f094-4b11-a207-d47e208c01c5 · outbound

This paper cites Are we ready for au- tonomous driving? The KITTI vision benchmark suite,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Are we ready for au- tonomous driving? The KITTI vision benchmark suite,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.697491Z

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Observation 7218007d-7293-47e8-9f59-b882096aabc3 · outbound

This paper cites Kubric: A scalable dataset generator,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Kubric: A scalable dataset generator,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.683467Z

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Observation 210794ed-489e-4eef-b6c7-d5203d98f2cf · outbound

This paper cites LIFT: learned invariant feature transform,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models LIFT: learned invariant feature transform,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.669838Z

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Observation ad2f4bba-09df-4a80-8fe7-d9baf4b13be9 · outbound

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

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models SuperPoint: Self- supervised interest point detection and description,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.655605Z

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Observation 36799e8f-f377-46c3-9af9-01912aa88735 · outbound

This paper cites Super- Glue: Learning feature matching with graph neural networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Super- Glue: Learning feature matching with graph neural networks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.642077Z

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Observation 6a85f75c-ec04-4e6b-b7f9-ff1e62ba0ba0 · outbound

This paper cites Decoupling makes weakly supervised local feature better,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Decoupling makes weakly supervised local feature better,

Reference 59

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This paper cites Efficient neighbourhood consensus networks via submanifold sparse convolutions,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Efficient neighbourhood consensus networks via submanifold sparse convolutions,

Reference 60

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Observation d766640d-214e-4f17-9eaa-363605ddda9b · outbound

This paper cites Dual-resolution corre- spondence networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Dual-resolution corre- spondence networks,

Reference 61

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Observation 28fa2dd2-b475-470e-a36e-e2839054320d · outbound

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

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models LoFTR: Detector- free local feature matching with transformers,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.586746Z

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Observation dad95db4-5419-490d-a859-d37851252af9 · outbound

This paper cites RoMa: Robust dense feature matching,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models RoMa: Robust dense feature matching,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.572849Z

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Observation 49dee2b5-e895-442e-994f-01674a519f24 · outbound

This paper cites DKM: dense kernelized feature matching for geometry estima- tion,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models DKM: dense kernelized feature matching for geometry estima- tion,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.558573Z

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Observation cb839b1d-3952-45eb-83bd-cf3e4d0751be · outbound

This paper cites Semi- dense feature matching with transformers and its applications in multiple-view geometry,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Semi- dense feature matching with transformers and its applications in multiple-view geometry,

Reference 65

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Observation 2468ec58-4be4-4f60-a583-a174b4e71078 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models DINOv2: Learning Robust Visual Features without Supervision

Reference 66

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Observation a4e8efd8-5551-4f15-9b8f-644493418ca4 · outbound

This paper cites Hierarchical discrete distribution decomposition for match density estimation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Hierarchical discrete distribution decomposition for match density estimation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.528870Z

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Observation a900fd7a-1f5a-41c0-99ed-8b61605be8d9 · outbound

This paper cites Learning accurate dense correspondences and when to trust them,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Learning accurate dense correspondences and when to trust them,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.514541Z

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Observation 685755d5-b55d-43d5-bc4f-c0d3f4b36656 · outbound

This paper cites PDC-Net+: Enhanced probabilistic dense correspondence network,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models PDC-Net+: Enhanced probabilistic dense correspondence network,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.501028Z

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Observation 56c50c65-c225-4a94-a936-abb17fbe0e02 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Learning transferable visual models from natural language supervision,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.487404Z

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Observation da9c8a8d-1d7d-4bd5-b9f1-9089dcb386d9 · outbound

This paper cites Segment Anything.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Segment Anything

Reference 71

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Observation 3bb9421e-1bce-4fdc-b7b1-64ab84224443 · outbound

This paper cites Depth Anything: Unleashing the power of large-scale unlabeled data,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Depth Anything: Unleashing the power of large-scale unlabeled data,

Reference 72

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Observation 3f1537e4-d460-4591-a8b6-2a5b96bd17e8 · outbound

This paper cites Vision transformer adapter for dense predictions,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Vision transformer adapter for dense predictions,

Reference 73

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Observation 288b6057-146d-4a78-b617-c77d02d16e29 · outbound

This paper cites Convolution meets lora: Parameter efficient finetuning for segment anything model,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Convolution meets lora: Parameter efficient finetuning for segment anything model,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.448122Z

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Observation 4c50c942-a0a5-40b9-957d-7b532f76bdbe · outbound

This paper cites Playing to Vision Foundation Model's Strengths in Stereo Matching.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Playing to Vision Foundation Model's Strengths in Stereo Matching

Reference 75

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Observation 222e93f0-ff81-486c-8cab-9efda67fc022 · outbound

This paper cites Stereo Any- where: Robust zero-shot deep stereo matching even where either stereo or mono fail,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Stereo Any- where: Robust zero-shot deep stereo matching even where either stereo or mono fail,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.434050Z

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Observation 967f44ce-76a1-4e73-ad1f-a84325082ab6 · outbound

This paper cites Depth Anything V2,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Depth Anything V2,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.332279Z

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Observation 95c7b3e9-dbb2-41d1-93c3-fe00a2fa5a9a · outbound

This paper cites SAMFlow: Eliminating any fragmentation in optical flow with segment anything model,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models SAMFlow: Eliminating any fragmentation in optical flow with segment anything model,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:16.227028Z

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Observation 4ebb7b36-2e87-4463-a4cb-f38407db18e1 · outbound

This paper cites Decon- volutional networks,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Decon- volutional networks,

Reference 79

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Observation dc2f5136-ce8f-4c81-91d1-ca5cd47fa350 · outbound

This paper cites Attention is all you need,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Attention is all you need,

Reference 80

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This paper cites A ConvNet for the 2020s,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A ConvNet for the 2020s,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:15.848283Z

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Observation 2bd1ce66-c1e3-4f58-a34c-54a9de1d4496 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Momentum contrast for unsupervised visual representation learning,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:15.667734Z

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Observation 5fb0403b-c482-47f8-ad6c-1095c8913e9f · outbound

This paper cites DynamicStereo: Consistent dynamic depth from stereo videos,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models DynamicStereo: Consistent dynamic depth from stereo videos,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:15.375570Z

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Observation 8c71ab7e-856c-47df-ad11-7f1d5b20d91f · outbound

This paper cites AccFlow: Backward accumulation for long-range optical flow,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models AccFlow: Backward accumulation for long-range optical flow,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:15.188045Z

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Observation 18ce96c3-1f3a-495f-8fe5-a81d276417c5 · outbound

This paper cites Vision trans- formers need registers,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Vision trans- formers need registers,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:15.096091Z

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Observation a76bf2fd-4260-4053-8eaf-3d891b9cfbde · outbound

This paper cites Decoupled weight decay regulariza- tion,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Decoupled weight decay regulariza- tion,

Reference 86

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Observation 0405c4db-49da-48c1-b89a-b17f38220222 · outbound

This paper cites Flow- Anything: Learning real-world optical flow estimation from large-scale single-view images,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Flow- Anything: Learning real-world optical flow estimation from large-scale single-view images,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.993926Z

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Observation 523a3ad8-0da1-4999-9dcd-be7385e23837 · outbound

This paper cites High-resolution stereo datasets with subpixel-accurate ground truth,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models High-resolution stereo datasets with subpixel-accurate ground truth,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.866368Z

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Observation 1a8b717f-2a85-4626-b18c-5b9c8df53244 · outbound

This paper cites A multi-view stereo benchmark with high-resolution images and multi-camera videos,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A multi-view stereo benchmark with high-resolution images and multi-camera videos,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.746901Z

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Observation b2c7b987-60ec-40d2-8473-fad5ab1b006e · outbound

This paper cites Infinite photorealistic worlds using procedural generation,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Infinite photorealistic worlds using procedural generation,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.648671Z

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Observation 95f0f366-90a3-4443-bef0-faae87752ec4 · outbound

This paper cites Spring: A high-resolution high-detail dataset and benchmark for scene flow, optical flow and stereo,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Spring: A high-resolution high-detail dataset and benchmark for scene flow, optical flow and stereo,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.547750Z

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Observation 6bd7c2d1-4e2d-4069-bf13-2fc8fc6945f8 · outbound

This paper cites ScanNet: Richly-annotated 3D reconstructions of indoor scenes,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models ScanNet: Richly-annotated 3D reconstructions of indoor scenes,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.429805Z

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Observation 7acd1d3c-d5fd-4c24-a444-9928d042f719 · outbound

This paper cites YFCC100M: the new data in multimedia research,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models YFCC100M: the new data in multimedia research,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.324374Z

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Observation 9251a0a4-6a90-4822-b5a5-89236745c504 · outbound

This paper cites WxBS: Wide baseline stereo generalizations,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models WxBS: Wide baseline stereo generalizations,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.219261Z

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Observation d46bf167-6370-4228-b13a-db4043a11b8f · outbound

This paper cites Open challenges in deep stereo: the Booster dataset,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Open challenges in deep stereo: the Booster dataset,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:14.084079Z

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Observation 2f76a102-3cb4-4564-86d2-3b0277423d40 · outbound

This paper cites Stereo Correspondence and Reconstruction of Endoscopic Data Challenge.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Stereo Correspondence and Reconstruction of Endoscopic Data Challenge

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T18:28:05.646183Z

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Observation a24ebcc5-0b88-4fe5-bf9f-3f632b9125dc · outbound

This paper cites Drivingstereo: A large-scale dataset for stereo matching in au- tonomous driving scenarios,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Drivingstereo: A large-scale dataset for stereo matching in au- tonomous driving scenarios,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:13.948994Z

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Observation b99dd61b-3ce8-48a4-96bd-4ab77fb4f2fd · outbound

This paper cites SUN3D: A database of big spaces reconstructed using sfm and object labels,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models SUN3D: A database of big spaces reconstructed using sfm and object labels,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:13.788626Z

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Observation 6c1d23ed-6f05-44db-86a2-7cbcc9c3b979 · outbound

This paper cites A benchmark for the evaluation of RGB-D SLAM systems,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models A benchmark for the evaluation of RGB-D SLAM systems,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:13.639169Z

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Observation f40e6da8-c91b-47df-aa13-7d7a32991b65 · outbound

This paper cites Flickr1024: A large-scale dataset for stereo image super-resolution,.

PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models Flickr1024: A large-scale dataset for stereo image super-resolution,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:28:13.488877Z

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Coverage vector

Reference resolution

Outbound references

Pith citing papers