Paper Citation Record · LEDGER

Learning Two-View Correspondences and Geometry Using Order-Aware Network

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:1908.04964.

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

pith.paper-citation-record.v1
1908.04964 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:34:15.689044Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:02:38.798084Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:02:38.942843Z

Reference resolution

47 of 47 outbound references displayed

verified exact1
verified fuzzy44
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60e3b6b8-001b-4a58-913a-5aa3ad23dd7e · outbound

This paper cites Gms: Grid-based motion statistics for fast, ultra-robust feature cor- respondence.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Gms: Grid-based motion statistics for fast, ultra-robust feature cor- respondence

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.129873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 846c5d06-4360-4854-968e-3b43fe0d0204 · outbound

This paper cites Dsac differentiable ransac for camera localization.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Dsac differentiable ransac for camera localization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.119620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 24b016ae-dee0-4b4c-89ad-e5e3a779aa19 · outbound

This paper cites Self-Improving Visual Odometry.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Self-Improving Visual Odometry

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:34:15.718319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69c2c052-5fe5-49f4-a3d1-bda2483f43d8 · outbound

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

Learning Two-View Correspondences and Geometry Using Order-Aware Network Superpoint: Self-supervised interest point detection and description

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.109761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ff99ce6-394f-402a-a8e1-ccf7a7352961 · outbound

This paper cites Splinecnn: Fast geometric deep learning with continuous b-spline kernels.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Splinecnn: Fast geometric deep learning with continuous b-spline kernels

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.101016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e781831a-3386-447a-997c-255ce11e06eb · outbound

This paper cites Random sample consensus: a paradigm for model ﬁtting with applications to image analysis and automated cartography.Communications of the ACM, 1981.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Random sample consensus: a paradigm for model ﬁtting with applications to image analysis and automated cartography.Communications of the ACM, 1981

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.092368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b055b109-114c-403a-a380-38d30c5127dd · outbound

This paper cites 3d semantic segmentation with submanifold sparse convolutional networks.

Learning Two-View Correspondences and Geometry Using Order-Aware Network 3d semantic segmentation with submanifold sparse convolutional networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.082989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ec341708-f276-4d16-9e9c-793de3dcd061 · outbound

This paper cites Inductive representation learning on large graphs.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Inductive representation learning on large graphs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.073461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc2c7132-f7b4-40f5-aae1-b151dfc81406 · outbound

This paper cites Multiple view ge- ometry in computer vision.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Multiple view ge- ometry in computer vision

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:34:15.574498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 640a8640-06cc-455a-ac5a-6d5d0b14a7cd · outbound

This paper cites Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset).

Learning Two-View Correspondences and Geometry Using Order-Aware Network Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.059651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3586162f-fcdd-4ba3-be15-7e4961aa0e7e · outbound

This paper cites Matrix backpropagation for deep networks with structured layers.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Matrix backpropagation for deep networks with structured layers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.051492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2fef77d5-d3e9-4e1f-b582-8110381727d1 · outbound

This paper cites Semi-supervised classiﬁ- cation with graph convolutional networks.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Semi-supervised classiﬁ- cation with graph convolutional networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.043163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3799ea54-3c7a-431d-b9a4-638053341da5 · outbound

This paper cites Escape from cells: Deep kd-networks for the recognition of 3d point cloud mod- els.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Escape from cells: Deep kd-networks for the recognition of 3d point cloud mod- els

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.034546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2f77e302-0ec5-4e74-8ff0-4f2d101d91af · outbound

This paper cites Undeepvo: Monocular visual odometry through unsuper- vised deep learning.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Undeepvo: Monocular visual odometry through unsuper- vised deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.025252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb7420cc-2f62-45fd-9f8b-abb9a5d96392 · outbound

This paper cites Bilateral func- tions for global motion modeling.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Bilateral func- tions for global motion modeling

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.015718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 65cd6691-99ca-4856-bd47-1951048a4973 · outbound

This paper cites A computer algorithm for reconstructing a scene from two projections.

Learning Two-View Correspondences and Geometry Using Order-Aware Network A computer algorithm for reconstructing a scene from two projections

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:16.007533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc494880-7be8-4cb6-bdb0-818fddcdace7 · outbound

This paper cites Distinctive image features from scale- invariant keypoints.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Distinctive image features from scale- invariant keypoints

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.998398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 739f1ace-699e-45c0-b3fd-147ddf9da2bb · outbound

This paper cites Contextdesc: Lo- cal descriptor augmentation with cross-modality context.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Contextdesc: Lo- cal descriptor augmentation with cross-modality context

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.989209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a32d9c16-78a9-4bbb-b55e-ca4cab0c9322 · outbound

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

Learning Two-View Correspondences and Geometry Using Order-Aware Network Geodesc: Learning local descriptors by integrating geometry constraints

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.978823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc017d2c-bde3-414c-821f-e00af943d3ea · outbound

This paper cites Geometric deep learning on graphs and manifolds using mixture model cnns.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Geometric deep learning on graphs and manifolds using mixture model cnns

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.967973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b437e90-4f4a-4e64-b418-4dd7492b2d14 · outbound

This paper cites Learning to ﬁnd good correspondences.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Learning to ﬁnd good correspondences

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.958386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f8b2101a-a129-4f06-b5d2-7bf219d364e3 · outbound

This paper cites Orb-slam: a versatile and accurate monocular slam system.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Orb-slam: a versatile and accurate monocular slam system

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.948778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ca459e5e-11bf-4eee-a7bf-c89434a6cbbe · outbound

This paper cites Learning convolutional neural networks for graphs.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Learning convolutional neural networks for graphs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.937833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6942d04c-961b-4102-8340-b489b0561915 · outbound

This paper cites Lf-net: learning local features from images.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Lf-net: learning local features from images

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.929688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b74c1bc-ccdd-4622-9bcb-8663f8e99945 · outbound

This paper cites Automatic differentiation in pytorch.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Automatic differentiation in pytorch

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T13:34:15.623454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06f8ae24-17b6-4423-9a63-d137665e2d8e · outbound

This paper cites Neural nearest neighbors net- works.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Neural nearest neighbors net- works

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.915931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34a3545c-208b-4ff8-a982-9178dbc74d84 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classiﬁcation and segmentation.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Pointnet: Deep learning on point sets for 3d classiﬁcation and segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.908161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84574679-2398-4904-ae7d-3d1e2972c5ee · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.899346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 389696cc-a9c3-4385-a36d-243aeaf0e61c · outbound

This paper cites Usac: a universal framework for random sample consensus.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Usac: a universal framework for random sample consensus

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.890157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dff72793-6422-48ae-951a-440bee4ee477 · outbound

This paper cites Deep fundamental matrix estimation.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Deep fundamental matrix estimation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.880443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69e618d9-3434-4b2e-bfdd-88ae781adbf1 · outbound

This paper cites Convo- lutional neural network architecture for geometric matching.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Convo- lutional neural network architecture for geometric matching

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.872911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e3c92bf5-65db-49e6-908f-67ea16eab768 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Learning Two-View Correspondences and Geometry Using Order-Aware Network U- net: Convolutional networks for biomedical image segmen- tation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.864535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c21c149d-611c-404b-9fec-30ed085cc3b9 · outbound

This paper cites Orb: An efﬁcient alternative to sift or surf.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Orb: An efﬁcient alternative to sift or surf

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.856019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f57dfc9-0ba0-40c0-b572-d053b46c9de4 · outbound

This paper cites Structure- from-motion revisited.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Structure- from-motion revisited

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.847803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f5abefe-0789-4ef1-abd4-836fee38828e · outbound

This paper cites Yfcc100m: the new data in multimedia research.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Yfcc100m: the new data in multimedia research

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.839347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dab59d14-e8d1-4d97-a0c6-9fea90f9a020 · outbound

This paper cites Lempitsky.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Lempitsky

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.831270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49c08628-2001-45ba-8a8d-981172d04c32 · outbound

This paper cites Demon: Depth and motion network for learning monocular stereo.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Demon: Depth and motion network for learning monocular stereo

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.823011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dec51620-c46b-47e3-981f-0155981cf4ee · outbound

This paper cites Visualsfm: A visual structure from motion system.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Visualsfm: A visual structure from motion system

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.814837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 02b556d3-1cab-4f35-9d10-022d82ed1f59 · outbound

This paper cites Sun3d: A database of big spaces reconstructed using sfm and object labels.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Sun3d: A database of big spaces reconstructed using sfm and object labels

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.804451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7e67613-fc8d-4c16-b251-90285aa71c50 · outbound

This paper cites Spidercnn: Deep learning on point sets with parameterized convolutional ﬁlters.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Spidercnn: Deep learning on point sets with parameterized convolutional ﬁlters

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.795105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46f99579-53ea-4d57-9ec2-5ede58ed4291 · outbound

This paper cites Lift: Learned invariant feature transform.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Lift: Learned invariant feature transform

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.784294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c63b7028-975a-4bbd-9210-9fba05197a2f · outbound

This paper cites Hierarchical graph rep- resentation learning with differentiable pooling.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Hierarchical graph rep- resentation learning with differentiable pooling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.775325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79731d0e-400b-408d-90e8-e1c17322eb23 · outbound

This paper cites Efﬁcient semantic scene comple- tion network with spatial group convolution.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Efﬁcient semantic scene comple- tion network with spatial group convolution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.766148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b8f93fe-c0f0-4c83-a0b2-5a73a3a73955 · outbound

This paper cites An end-to-end deep learning architecture for graph classiﬁcation.

Learning Two-View Correspondences and Geometry Using Order-Aware Network An end-to-end deep learning architecture for graph classiﬁcation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.756914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd3de041-fc51-45dc-a905-047356cc97c8 · outbound

This paper cites Learn- ing and matching multi-view descriptors for registration of point clouds.

Learning Two-View Correspondences and Geometry Using Order-Aware Network Learn- ing and matching multi-view descriptors for registration of point clouds

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.748717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2c31dc5a-218b-4d99-b497-363d51c8d4e3 · outbound

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

Learning Two-View Correspondences and Geometry Using Order-Aware Network Unsupervised learning of depth and ego-motion from video

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.739283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58a8b039-07ef-4b50-b4d9-6da2297d1f71 · outbound

This paper cites Supplementary appendix A.1 Weighted Eight-Point Algorithm Here we provide a detailed description of the weighted eight-point algorithm [21].

Learning Two-View Correspondences and Geometry Using Order-Aware Network Supplementary appendix A.1 Weighted Eight-Point Algorithm Here we provide a detailed description of the weighted eight-point algorithm [21]

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:34:15.729649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 51868500-105f-4414-bb41-4911ac714739 · inbound

TurboReg: TurboClique for Robust and Efficient Point Cloud Registration cites this paper.

TurboReg: TurboClique for Robust and Efficient Point Cloud Registration Learning Two-View Correspondences and Geometry Using Order-Aware Network

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:02:38.947011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.