Paper Citation Record · LEDGER

Cross-View Completion Models are Zero-shot Correspondence Estimators

As of 17 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 4 inbound Pith citation observations for arXiv:2412.09072.

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

pith.paper-citation-record.v1
2412.09072 v1

Coverage vector

measured 100 of 103 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:26:22.572375Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:13:38.624015Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:14:01.779436Z

Reference resolution

100 of 103 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a278511-167a-4492-8a3b-c03efbf24fee · outbound

This paper cites Multimae: Multi-modal multi-task masked autoencoders.

Cross-View Completion Models are Zero-shot Correspondence Estimators Multimae: Multi-modal multi-task masked autoencoders

Reference 1

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Observation e13609e6-28ff-4d33-a739-49868178c996 · outbound

This paper cites Multi-view depth estimation by fusing single-view depth probability with multi-view geometry.

Cross-View Completion Models are Zero-shot Correspondence Estimators Multi-view depth estimation by fusing single-view depth probability with multi-view geometry

Reference 2

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Observation 44ff660a-72b7-4820-bcc5-448ffac78af2 · outbound

This paper cites Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors.

Cross-View Completion Models are Zero-shot Correspondence Estimators Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors

Reference 3

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Observation ebec7e63-d5a5-4a7f-9236-c9d814698bff · outbound

This paper cites Dualrefine: Self-supervised depth and pose estima- tion through iterative epipolar sampling and refinement to- ward equilibrium.

Cross-View Completion Models are Zero-shot Correspondence Estimators Dualrefine: Self-supervised depth and pose estima- tion through iterative epipolar sampling and refinement to- ward equilibrium

Reference 4

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Observation 40b7943d-66e3-40be-859a-d012eac8273d · outbound

This paper cites Beit: Bert pre- training of image transformers.

Cross-View Completion Models are Zero-shot Correspondence Estimators Beit: Bert pre- training of image transformers

Reference 5

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Observation a306bf00-8351-4ebf-a368-4f7d50e9e81b · outbound

This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 6

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Observation c4ccf75b-84bd-474a-9168-3deb3b6d8f2f · outbound

This paper cites Unsupervised monocular depth and ego-motion learning with structure and semantics.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised monocular depth and ego-motion learning with structure and semantics

Reference 7

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Observation fb3251d3-9b5a-4595-be1c-680e12830fbf · outbound

This paper cites Adaptive fusion of single-view and multi-view depth for autonomous driving.

Cross-View Completion Models are Zero-shot Correspondence Estimators Adaptive fusion of single-view and multi-view depth for autonomous driving

Reference 8

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Observation c1c8bfb6-2396-4986-b2cf-90b15c57cddf · outbound

This paper cites Cats: Cost aggregation transformers for visual correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Cats: Cost aggregation transformers for visual correspondence

Reference 9

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Observation aea1e8e9-1f92-4a44-94fb-dd77372437ac · outbound

This paper cites Cats++: Boosting cost aggregation with convolutions and transformers.

Cross-View Completion Models are Zero-shot Correspondence Estimators Cats++: Boosting cost aggregation with convolutions and transformers

Reference 10

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Observation d69305c4-a786-4bd0-b141-901bcb7e590b · outbound

This paper cites Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Cat- seg: Cost aggregation for open-vocabulary semantic seg- mentation

Reference 11

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Observation e61697df-5275-43cf-8d61-ddbbd1906d6d · outbound

This paper cites Emerging property of masked token for effective pre-training.

Cross-View Completion Models are Zero-shot Correspondence Estimators Emerging property of masked token for effective pre-training

Reference 12

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Observation 61ba3a4e-3904-4972-b89f-ee177336d404 · outbound

This paper cites Salience-based adaptive masking: re- visiting token dynamics for enhanced pre-training.

Cross-View Completion Models are Zero-shot Correspondence Estimators Salience-based adaptive masking: re- visiting token dynamics for enhanced pre-training

Reference 13

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Observation 637a174d-48a6-4207-ac98-fa87268e1bb8 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Cross-View Completion Models are Zero-shot Correspondence Estimators The cityscapes dataset for semantic urban scene understanding

Reference 14

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Observation 698c7939-5f6f-4765-a3d8-65ad396dc386 · outbound

This paper cites Vision Transformers Need Registers.

Cross-View Completion Models are Zero-shot Correspondence Estimators Vision Transformers Need Registers

Reference 15

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Observation 5750c8b5-f27e-4950-9a20-8e7724a0eca9 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Cross-View Completion Models are Zero-shot Correspondence Estimators BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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Observation 7f0c2a43-6964-4804-8eb9-0f5a0286a0e8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Cross-View Completion Models are Zero-shot Correspondence Estimators An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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Observation 6af82e26-79a9-4ebe-ad8a-2757c4fc90bd · outbound

This paper cites Dkm: Dense kernelized feature matching for geometry estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Dkm: Dense kernelized feature matching for geometry estimation

Reference 18

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Observation 33ecf33d-4b03-4d18-b777-9dcbabb00b10 · outbound

This paper cites Roma: Robust dense feature matching.

Cross-View Completion Models are Zero-shot Correspondence Estimators Roma: Robust dense feature matching

Reference 19

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Observation ebb91a23-487a-44a8-9a36-49b55ff68040 · outbound

This paper cites Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture.

Cross-View Completion Models are Zero-shot Correspondence Estimators Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture

Reference 20

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Observation 4ac7fb32-0f4c-450b-9297-38f2727442ec · outbound

This paper cites Probing the 3d awareness of visual foundation models.

Cross-View Completion Models are Zero-shot Correspondence Estimators Probing the 3d awareness of visual foundation models

Reference 21

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Observation bb1c2ac9-8252-4c2e-8f50-b581824df2a5 · outbound

This paper cites Single-view and multi-view depth fusion.

Cross-View Completion Models are Zero-shot Correspondence Estimators Single-view and multi-view depth fusion

Reference 22

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Observation 93311a26-a4e7-4e66-b674-c2903da98d2c · outbound

This paper cites Corrupted Image Modeling for Self-Supervised Visual Pre-Training.

Cross-View Completion Models are Zero-shot Correspondence Estimators Corrupted Image Modeling for Self-Supervised Visual Pre-Training

Reference 23

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Observation 9ba6b1f7-88bb-4ac3-8ffe-ed00a835a2dc · outbound

This paper cites Disentangling object motion and occlusion for unsupervised multi-frame monocular depth.

Cross-View Completion Models are Zero-shot Correspondence Estimators Disentangling object motion and occlusion for unsupervised multi-frame monocular depth

Reference 24

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Observation 70cadec9-abe5-4f84-be21-aa3e91f6d8dc · outbound

This paper cites Vision meets robotics: The kitti dataset.The Inter- national Journal of Robotics Research, 32(11):1231–1237,.

Cross-View Completion Models are Zero-shot Correspondence Estimators Vision meets robotics: The kitti dataset.The Inter- national Journal of Robotics Research, 32(11):1231–1237,

Reference 25

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Observation b5cef6a7-d87c-4478-9bdc-3c4951dd6646 · outbound

This paper cites Unsupervised monocular depth estimation with left- right consistency.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised monocular depth estimation with left- right consistency

Reference 26

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Observation e7cbbc60-a5eb-49af-a682-c6c6559b6464 · outbound

This paper cites Digging into self-supervised monocular 10 depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Digging into self-supervised monocular 10 depth estimation

Reference 27

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Observation 1b7d05a7-ff36-41c4-b87d-891f4b265fb8 · outbound

This paper cites Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras.

Cross-View Completion Models are Zero-shot Correspondence Estimators Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras

Reference 28

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Observation 4f6181ce-7d1d-48c2-90f8-5b28aaced9ba · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Cross-View Completion Models are Zero-shot Correspondence Estimators Bootstrap your own latent-a new approach to self-supervised learning

Reference 29

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Observation 29f32d1d-2fc5-4c56-88a0-d57eaddd003d · outbound

This paper cites 3d packing for self-supervised monocular depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators 3d packing for self-supervised monocular depth estimation

Reference 30

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Observation b6527152-a654-4da5-b99b-bc67a005a38a · outbound

This paper cites Geometric unsupervised domain adaptation for semantic segmentation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Geometric unsupervised domain adaptation for semantic segmentation

Reference 31

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Cross-View Completion Models are Zero-shot Correspondence Estimators Multi-frame self-supervised depth with transformers

Reference 32

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Observation 54a9a9f2-8eaa-4029-940c-9b3c944d6cd1 · outbound

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Cross-View Completion Models are Zero-shot Correspondence Estimators Siamese masked autoencoders

Reference 33

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This paper cites Few-shot object de- tection with foundation models.

Cross-View Completion Models are Zero-shot Correspondence Estimators Few-shot object de- tection with foundation models

Reference 34

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This paper cites Deep residual learning for image recognition.

Cross-View Completion Models are Zero-shot Correspondence Estimators Deep residual learning for image recognition

Reference 35

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Cross-View Completion Models are Zero-shot Correspondence Estimators Momentum contrast for unsupervised visual rep- resentation learning

Reference 36

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This paper cites Masked autoencoders are scal- able vision learners.

Cross-View Completion Models are Zero-shot Correspondence Estimators Masked autoencoders are scal- able vision learners

Reference 37

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This paper cites Ra-depth: Resolution adaptive self-supervised monocular depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Ra-depth: Resolution adaptive self-supervised monocular depth estimation

Reference 38

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Observation e6ec740e-41e1-40a6-b503-996accc0760f · outbound

This paper cites Stereo processing by semiglobal matching and mutual information.

Cross-View Completion Models are Zero-shot Correspondence Estimators Stereo processing by semiglobal matching and mutual information

Reference 39

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Observation a6a34b6a-9e23-4aae-8499-58940ae2a2b2 · outbound

This paper cites Deep matching prior: Test-time optimization for dense correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Deep matching prior: Test-time optimization for dense correspondence

Reference 40

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This paper cites Cost aggregation with 4d convolutional swin transformer for few-shot segmentation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Cost aggregation with 4d convolutional swin transformer for few-shot segmentation

Reference 41

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This paper cites Neural matching fields: Implicit representation of matching fields for visual correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Neural matching fields: Implicit representation of matching fields for visual correspondence

Reference 42

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Observation ca0775a4-17cb-4295-bdf2-8ba02ecd7a17 · outbound

This paper cites Unifying feature and cost aggregation with transformers for semantic and visual correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unifying feature and cost aggregation with transformers for semantic and visual correspondence

Reference 43

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This paper cites Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume.

Cross-View Completion Models are Zero-shot Correspondence Estimators Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume

Reference 44

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This paper cites Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova.

Cross-View Completion Models are Zero-shot Correspondence Estimators Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova

Reference 45

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Observation 8db5b47b-3e53-4dd8-a8de-bc90cfd05f4f · outbound

This paper cites Re- purposing diffusion-based image generators for monocular depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Re- purposing diffusion-based image generators for monocular depth estimation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.214011Z

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Observation 8360aff0-3df1-4f15-be12-b4f7ac8b0299 · outbound

This paper cites Recurrent transformer net- works for semantic correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Recurrent transformer net- works for semantic correspondence

Reference 47

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Observation dbef4299-76b3-476c-8098-95859f58fe2c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Cross-View Completion Models are Zero-shot Correspondence Estimators Adam: A Method for Stochastic Optimization

Reference 48

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This paper cites Cottereau, and Wei Tsang Ooi.

Cross-View Completion Models are Zero-shot Correspondence Estimators Cottereau, and Wei Tsang Ooi

Reference 49

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Observation 8256a336-a18c-4c9e-964b-a1512e0d2945 · outbound

This paper cites Sfnet: Learning object-aware semantic correspon- dence.

Cross-View Completion Models are Zero-shot Correspondence Estimators Sfnet: Learning object-aware semantic correspon- dence

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.184256Z

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Observation 6c1665bb-cf59-4e64-bd1c-b3c15adcf5b4 · outbound

This paper cites Grounding Image Matching in 3D with MASt3R.

Cross-View Completion Models are Zero-shot Correspondence Estimators Grounding Image Matching in 3D with MASt3R

Reference 51

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This paper cites Unsupervised monocular depth learn- ing in dynamic scenes.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised monocular depth learn- ing in dynamic scenes

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.174565Z

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Observation 43ab95c4-8edb-479d-9954-be8230f0ef8e · outbound

This paper cites Learning to fuse monocular and multi-view cues for multi- frame depth estimation in dynamic scenes.

Cross-View Completion Models are Zero-shot Correspondence Estimators Learning to fuse monocular and multi-view cues for multi- frame depth estimation in dynamic scenes

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.164419Z

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Observation 116990f6-919b-449b-8a63-8eb56a212ff0 · outbound

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

Cross-View Completion Models are Zero-shot Correspondence Estimators Megadepth: Learning single-view depth prediction from internet photos

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.154061Z

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Observation e30033df-043a-449b-ae8a-b29b63f80c20 · outbound

This paper cites Self- low: Self-supervised learning of optical flow.

Cross-View Completion Models are Zero-shot Correspondence Estimators Self- low: Self-supervised learning of optical flow

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.144006Z

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Observation 6ae2e18f-22d7-4c24-9a2b-7a1207bcf841 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Cross-View Completion Models are Zero-shot Correspondence Estimators Swin transformer: Hierarchical vision transformer using shifted windows

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.133711Z

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Observation f20fe20b-0b34-4949-b8e4-ce1847113a07 · outbound

This paper cites Flowdiffuser: Advancing optical flow estimation with diffusion models.

Cross-View Completion Models are Zero-shot Correspondence Estimators Flowdiffuser: Advancing optical flow estimation with diffusion models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.123768Z

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Observation 384962cc-366c-4b41-89a1-8a768ff4bb87 · outbound

This paper cites Unflow: Un- supervised learning of optical flow with a bidirectional cen- sus loss.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unflow: Un- supervised learning of optical flow with a bidirectional cen- sus loss

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.112400Z

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Observation f87b6dc8-902a-4c88-94e1-a4249c2272dc · outbound

This paper cites Dgc-net: Dense geometric correspondence network.

Cross-View Completion Models are Zero-shot Correspondence Estimators Dgc-net: Dense geometric correspondence network

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.101637Z

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Observation f86e295d-750b-437e-8f76-eb99f0f5144d · outbound

This paper cites Hypercorrela- tion squeeze for few-shot segmentation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Hypercorrela- tion squeeze for few-shot segmentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.090974Z

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Observation 768f6d8a-7179-4f32-a1cc-527cd4006e4f · outbound

This paper cites Efficientps: Efficient panoptic segmentation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Efficientps: Efficient panoptic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.079918Z

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Observation 6815345d-1feb-4720-8d19-e998907b5e03 · outbound

This paper cites Diffusion Model for Dense Matching.

Cross-View Completion Models are Zero-shot Correspondence Estimators Diffusion Model for Dense Matching

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T17:26:22.432059Z

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Observation 585be47b-76c1-4404-a83e-f1c2dffc484d · outbound

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Cross-View Completion Models are Zero-shot Correspondence Estimators Unresolved cited work

Reference 63

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Observation 73a43ba4-3899-4b14-8fb8-f2fbac38920e · outbound

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Cross-View Completion Models are Zero-shot Correspondence Estimators Automatic differentiation in pytorch

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.058525Z

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Observation a0f16e01-6705-4a6f-b951-bb6191cbee99 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Cross-View Completion Models are Zero-shot Correspondence Estimators Pytorch: An imperative style, high-performance deep learning library

Reference 65

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Observation fb037e9b-e1e9-4a5c-acd4-33641c652634 · outbound

This paper cites Context encoders: Feature learning by inpainting.

Cross-View Completion Models are Zero-shot Correspondence Estimators Context encoders: Feature learning by inpainting

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.041478Z

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Observation 74915db1-cee7-4418-bcf8-31070ac2e815 · outbound

This paper cites Don’t forget the past: Recurrent depth esti- mation from monocular video.

Cross-View Completion Models are Zero-shot Correspondence Estimators Don’t forget the past: Recurrent depth esti- mation from monocular video

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.030207Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b59676a7-eab2-4598-956e-3301c2d627c8 · outbound

This paper cites Unidepth: Universal monocular metric depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unidepth: Universal monocular metric depth estimation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.018469Z

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Observation 90a33d30-8629-4759-8996-5560876d457c · outbound

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Cross-View Completion Models are Zero-shot Correspondence Estimators Movie Gen: A Cast of Media Foundation Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T17:26:22.457490Z

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Observation 4177e0ab-f453-427d-bd34-0f0d1e335a1b · outbound

This paper cites Vi- sion transformers for dense prediction.

Cross-View Completion Models are Zero-shot Correspondence Estimators Vi- sion transformers for dense prediction

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:23.007625Z

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Observation 1c41c84a-a03f-41aa-ae61-26206d3c93cb · outbound

This paper cites Unsupervised deep learning for optical flow estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised deep learning for optical flow estimation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.996682Z

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Observation b1407576-e942-4935-92fb-d3bc5f5e28a8 · outbound

This paper cites Sacreg: Scene-agnostic co- ordinate regression for visual localization.

Cross-View Completion Models are Zero-shot Correspondence Estimators Sacreg: Scene-agnostic co- ordinate regression for visual localization

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.986065Z

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Observation a1ff8899-7f51-4245-82cb-72feec59d2b9 · outbound

This paper cites Neighbourhood consensus networks.

Cross-View Completion Models are Zero-shot Correspondence Estimators Neighbourhood consensus networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.974626Z

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Observation 9487e71d-818b-46a7-a362-f82c7fb7f7ce · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Cross-View Completion Models are Zero-shot Correspondence Estimators High-resolution image synthesis with latent diffusion models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T17:26:22.475272Z

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Observation fe538e13-a490-4bc3-b1f1-67a9bcecc70a · outbound

This paper cites Attention meets geometry: Geom- etry guided spatial-temporal attention for consistent self- supervised monocular depth estimation.

Cross-View Completion Models are Zero-shot Correspondence Estimators Attention meets geometry: Geom- etry guided spatial-temporal attention for consistent self- supervised monocular depth estimation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.957187Z

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Observation b9e308b0-1351-4b6d-b4b9-b6e75ba0e0fa · outbound

This paper cites A multi-view stereo benchmark with high- 12 resolution images and multi-camera videos.

Cross-View Completion Models are Zero-shot Correspondence Estimators A multi-view stereo benchmark with high- 12 resolution images and multi-camera videos

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.946200Z

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Observation d827e27e-e391-4070-b7ed-0fd07dbb14a7 · outbound

This paper cites Ransac-flow: generic two-stage image alignment.

Cross-View Completion Models are Zero-shot Correspondence Estimators Ransac-flow: generic two-stage image alignment

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.935786Z

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Observation 783d4860-590b-4c68-a735-2750f802c800 · outbound

This paper cites Emergent correspondence from image diffusion.

Cross-View Completion Models are Zero-shot Correspondence Estimators Emergent correspondence from image diffusion

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.925571Z

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Observation 5a17d202-c663-40b5-a271-348e9fb3e2ab · outbound

This paper cites Gocor: Bringing globally optimized correspon- dence volumes into your neural network.

Cross-View Completion Models are Zero-shot Correspondence Estimators Gocor: Bringing globally optimized correspon- dence volumes into your neural network

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.914128Z

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Observation 8a3419b4-31a4-4862-9dfa-d4ba005cfa17 · outbound

This paper cites Glu- net: Global-local universal network for dense flow and cor- respondences.

Cross-View Completion Models are Zero-shot Correspondence Estimators Glu- net: Global-local universal network for dense flow and cor- respondences

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.902872Z

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Observation ac347442-4a5a-4173-9566-cc618ec9fe78 · outbound

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

Cross-View Completion Models are Zero-shot Correspondence Estimators Learning accurate dense correspondences and when to trust them

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.891877Z

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Observation 409eabc7-e094-42ad-9f11-3d52778dd998 · outbound

This paper cites Pdc-net+: Enhanced probabilistic dense corre- spondence network.

Cross-View Completion Models are Zero-shot Correspondence Estimators Pdc-net+: Enhanced probabilistic dense corre- spondence network

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.880701Z

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Observation ca55503d-88f3-49cd-921a-f78293ef6dae · outbound

This paper cites Attention is all you need.

Cross-View Completion Models are Zero-shot Correspondence Estimators Attention is all you need

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.869509Z

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Observation 34b5fbda-2891-4b61-ae3c-988eea59e981 · outbound

This paper cites Dust3r: Geometric 3d vision made easy.

Cross-View Completion Models are Zero-shot Correspondence Estimators Dust3r: Geometric 3d vision made easy

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.858624Z

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Observation cb69555a-3c7d-47d7-baf2-e9328e0e26d9 · outbound

This paper cites Crafting monocular cues and velocity guidance for self-supervised multi-frame depth learning.

Cross-View Completion Models are Zero-shot Correspondence Estimators Crafting monocular cues and velocity guidance for self-supervised multi-frame depth learning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.848111Z

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Observation 6f9841eb-7b23-4c4f-abd1-8820a8de76bc · outbound

This paper cites Unos: Unified unsupervised optical- flow and stereo-depth estimation by watching videos.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unos: Unified unsupervised optical- flow and stereo-depth estimation by watching videos

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.837259Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f07ccbe8-4e12-4ef5-a731-68e2d9370391 · outbound

This paper cites Sea-raft: Simple, efficient, accurate raft for optical flow.

Cross-View Completion Models are Zero-shot Correspondence Estimators Sea-raft: Simple, efficient, accurate raft for optical flow

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.825487Z

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Observation 9cc1edc1-1fd9-4166-821e-90e2768c4d88 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Cross-View Completion Models are Zero-shot Correspondence Estimators Image quality assessment: from error visibility to structural similarity

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.814884Z

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Observation 70fc410d-ddbc-4fcf-a93d-f93ae123688b · outbound

This paper cites The temporal opportunist: Self-supervised multi-frame monocular depth.

Cross-View Completion Models are Zero-shot Correspondence Estimators The temporal opportunist: Self-supervised multi-frame monocular depth

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.804471Z

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Observation 9262cfd7-2416-44e4-86c9-006ef6eefb93 · outbound

This paper cites Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion.

Cross-View Completion Models are Zero-shot Correspondence Estimators Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.794153Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b88df8be-7653-4bde-805c-94a2869e6746 · outbound

This paper cites Croco v2: Improved cross-view completion pre- training for stereo matching and optical flow.

Cross-View Completion Models are Zero-shot Correspondence Estimators Croco v2: Improved cross-view completion pre- training for stereo matching and optical flow

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.783895Z

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Observation 4b1b9312-35ec-410b-afeb-0bffd2601fb0 · outbound

This paper cites Gmflow: Learning optical flow via global matching.

Cross-View Completion Models are Zero-shot Correspondence Estimators Gmflow: Learning optical flow via global matching

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T17:26:22.545373Z

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Observation 2684c6e6-7806-4cbf-b686-55f80262d35b · outbound

This paper cites Depth anything: Un- leashing the power of large-scale unlabeled data.

Cross-View Completion Models are Zero-shot Correspondence Estimators Depth anything: Un- leashing the power of large-scale unlabeled data

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.767323Z

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Observation ce5ea5eb-6074-45ba-9a60-4244e0706148 · outbound

This paper cites Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions.

Cross-View Completion Models are Zero-shot Correspondence Estimators Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.757508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5a959ecb-1ffb-48eb-8740-1fadc0c78d2a · outbound

This paper cites Met- ric3d: Towards zero-shot metric 3d prediction from a sin- gle image.

Cross-View Completion Models are Zero-shot Correspondence Estimators Met- ric3d: Towards zero-shot metric 3d prediction from a sin- gle image

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.746545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation db8c6e6b-0a0f-41bb-91a7-63db35eeebf7 · outbound

This paper cites Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness.

Cross-View Completion Models are Zero-shot Correspondence Estimators Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.734988Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3459da9-d212-4328-b1eb-88facfa9ebb1 · outbound

This paper cites A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence.

Cross-View Completion Models are Zero-shot Correspondence Estimators A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.723949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 015c1a55-f16a-449a-80c3-93c8c983c437 · outbound

This paper cites Monovit: Self-supervised monocular depth estimation with a vision transformer.

Cross-View Completion Models are Zero-shot Correspondence Estimators Monovit: Self-supervised monocular depth estimation with a vision transformer

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.711780Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d0c66f9-a0c8-44d4-ad22-09591cd984fb · outbound

This paper cites Unsupervised learning of depth and ego- motion from video.

Cross-View Completion Models are Zero-shot Correspondence Estimators Unsupervised learning of depth and ego- motion from video

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:26:22.700496Z

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Observation f3eac1cc-1a12-42ab-808e-61366ae768c3 · outbound

This paper cites A survey on open- vocabulary detection and segmentation: Past, present, and future.

Cross-View Completion Models are Zero-shot Correspondence Estimators A survey on open- vocabulary detection and segmentation: Past, present, and future

Reference 100

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T17:26:22.689019Z

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Pith citing papers

Observation 720025fd-2594-439a-b772-c81b13b132b4 · inbound

Emergent Temporal Correspondences from Video Diffusion Transformers cites this paper.

Emergent Temporal Correspondences from Video Diffusion Transformers Cross-View Completion Models are Zero-shot Correspondence Estimators

Reference 3

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Observation 793d6e31-3cfb-4f99-abc5-10f3afd64398 · inbound

TTT3R: 3D Reconstruction as Test-Time Training cites this paper.

TTT3R: 3D Reconstruction as Test-Time Training Cross-View Completion Models are Zero-shot Correspondence Estimators

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T06:41:16.480288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 125283ca-d290-4258-8a41-986dd796b577 · inbound

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos cites this paper.

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos Cross-View Completion Models are Zero-shot Correspondence Estimators

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:31:10.218383Z

Source-reported events for the cited work

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Observation bfdd6d7f-ab69-4285-8540-5a3514cb442c · inbound

Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction cites this paper.

Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction Cross-View Completion Models are Zero-shot Correspondence Estimators

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:01.780879Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.