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Paper Citation Record · LEDGER

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:1909.00866.

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

pith.paper-citation-record.v1
1909.00866 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:38:47.078260Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c0c62ba-54d1-489b-821e-469c340ec36b · outbound

This paper cites A meta-analysis of crop yield under climate change and adaptation.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds A meta-analysis of crop yield under climate change and adaptation

Reference 1

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Observation 5c6cb3e9-0f03-422f-b918-7944d9723791 · outbound

This paper cites Lights, camera, action: high-throughput plant phenotyping is ready for a close-up.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Lights, camera, action: high-throughput plant phenotyping is ready for a close-up

Reference 2

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Observation 7d1c1c17-17e7-42f5-a1a4-f9064be37a41 · outbound

This paper cites Image analysis: the new bottleneck in plant phenotyping [applications corner].

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Image analysis: the new bottleneck in plant phenotyping [applications corner]

Reference 3

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Observation 6763823f-a27b-4169-8480-c21dd1b0438c · outbound

This paper cites Imaging system for classification of local flora of uttarakhand region.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Imaging system for classification of local flora of uttarakhand region

Reference 4

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Observation 9122eb5a-fc31-4cf6-9846-36c1a1196bd1 · outbound

This paper cites Novel low cost 3d surface model reconstruction system for plant phenotyping.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Novel low cost 3d surface model reconstruction system for plant phenotyping

Reference 5

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Observation 29974b71-76ef-4648-ad37-2230b7c0679c · outbound

This paper cites Local shape feature fusion for improved matching, pose estimation and 3d object recognition.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Local shape feature fusion for improved matching, pose estimation and 3d object recognition

Reference 6

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Observation 84a2a0cc-deaf-446b-a2d4-dfe059a42c28 · outbound

This paper cites Fast descriptors and correspondence propagation for robust global point cloud registration.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Fast descriptors and correspondence propagation for robust global point cloud registration

Reference 7

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Observation 605be9dc-8a20-41c4-ba30-b59999f8bec0 · outbound

This paper cites Data-driven 3d voxel patterns for object category recognition.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Data-driven 3d voxel patterns for object category recognition

Reference 8

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Observation 6c6158a6-0361-45b1-a95e-1189788fab85 · outbound

This paper cites In search of inliers: 3d corre- spondence by local and global voting.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds In search of inliers: 3d corre- spondence by local and global voting

Reference 9

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Observation 88b29d28-9a2e-4bc7-97d9-15174b33ada7 · outbound

This paper cites Performance evaluation of 3d correspondence grouping algorithms.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Performance evaluation of 3d correspondence grouping algorithms

Reference 10

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Observation f0f1c727-6cee-4413-8f30-3f60ee8d8989 · outbound

This paper cites Fast matching of binary features.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Fast matching of binary features

Reference 11

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Observation 91de1a3b-fd6f-4455-b617-9a23060fd885 · outbound

This paper cites Robustifying corre- spondence based 6d object pose estimation.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Robustifying corre- spondence based 6d object pose estimation

Reference 12

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This paper cites Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Reference 13

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Observation 9c1488f3-15a9-482d-9321-6de32a2ef9fc · outbound

This paper cites 3d free-form object recognition in range images using local surface patches.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds 3d free-form object recognition in range images using local surface patches

Reference 14

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Observation ad068967-a6f1-40e7-976c-f7c5e4ae5ba2 · outbound

This paper cites Ranking 3d feature correspondences via consistency voting.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Ranking 3d feature correspondences via consistency voting

Reference 15

Resolution
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Source-reported events for the cited work

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Observation a33fcfb0-667f-421c-9163-9ccf79e7b632 · outbound

This paper cites Object recognition in 3d scenes with occlusions and clutter by hough voting.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Object recognition in 3d scenes with occlusions and clutter by hough voting

Reference 16

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Observation 6b465968-56d8-484f-a334-aba90a33359a · outbound

This paper cites Rotational subgroup voting and pose clustering for robust 3d object recognition.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Rotational subgroup voting and pose clustering for robust 3d object recognition

Reference 17

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Observation dbfb74a0-1084-4f77-b75c-74245285ebd1 · outbound

This paper cites Mlesac: A new robust estimator with application to estimating image geometry.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Mlesac: A new robust estimator with application to estimating image geometry

Reference 18

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Observation 1d8a0cba-0afe-486a-950e-954e469bdf93 · outbound

This paper cites Research on sift image matching based on mlesac algorithm.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Research on sift image matching based on mlesac algorithm

Reference 19

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Observation e0a289f1-ee98-439a-a896-7b50196616f4 · outbound

This paper cites Feature correspondence via graph matching: Models and global optimization.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Feature correspondence via graph matching: Models and global optimization

Reference 20

Resolution
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Source-reported events for the cited work

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Observation 2dc5dfae-fc4a-46e8-8343-54ca933c6ec1 · outbound

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

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Distinctive image features from scale-invariant keypoints

Reference 21

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Observation a4efa956-6959-4a47-aa2c-941cd1bc6b72 · outbound

This paper cites An efficient ransac for 3d object recognition in noisy and occluded scenes.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds An efficient ransac for 3d object recognition in noisy and occluded scenes

Reference 22

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Observation 3336c383-b964-4194-a0e1-094c02854588 · outbound

This paper cites Surface matching for object recognition in complex 3-d scenes.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Surface matching for object recognition in complex 3-d scenes

Reference 23

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Source-reported events for the cited work

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Observation 0e805fdc-6970-4fc3-b1b2-de68cb2f9537 · outbound

This paper cites Method and means for recognizing complex patterns, December 18 1962.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Method and means for recognizing complex patterns, December 18 1962

Reference 24

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Source-reported events for the cited work

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Observation 27262ca5-2088-48e6-864f-809f6962f358 · outbound

This paper cites 3d scans of plant shoot architectures, Jul 2017.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds 3d scans of plant shoot architectures, Jul 2017

Reference 25

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Source-reported events for the cited work

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Observation 4bb580b4-54bc-49f0-8a8e-6789a4421882 · outbound

This paper cites Performance evaluation of 3d keypoint detectors.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds Performance evaluation of 3d keypoint detectors

Reference 26

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Source-reported events for the cited work

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This paper cites A novel representation and feature matching algorithm for automatic pairwise registration of range images.International Journal of Computer Vision, 66(1):19– 40, 2006.

Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds A novel representation and feature matching algorithm for automatic pairwise registration of range images.International Journal of Computer Vision, 66(1):19– 40, 2006

Reference 27

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