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Paper Citation Record · LEDGER

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2511.21666.

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

pith.paper-citation-record.v1
2511.21666 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:52:00.934502Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:38:33.342354Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T12:03:23.826629Z

Reference resolution

43 of 43 outbound references displayed

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Outbound references

Observation 072cf312-5762-4c07-a52e-03030b986f13 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 1

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Observation 2c01fcea-615a-4995-bd3e-5bdd0723795f · outbound

This paper cites DROP: Dexterous Reorientation via Online Planning.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds DROP: Dexterous Reorientation via Online Planning

Reference 2

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Observation cf82d55a-e37d-444d-a983-80734d641f25 · outbound

This paper cites Peng,Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey, preprint, (2023).

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Peng,Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey, preprint, (2023)

Reference 3

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Observation 1b3524fe-4459-4694-9634-4de6686f59f2 · outbound

This paper cites Fast Uncertainty Quantification for Deep Object Pose Estimation.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Fast Uncertainty Quantification for Deep Object Pose Estimation

Reference 4

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Observation 1c187cc4-7259-44b4-a46f-c6b10b716859 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 5

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Observation 26978c3e-e838-4aac-a311-f661250621cc · outbound

This paper cites Brachmann, F.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Brachmann, F

Reference 6

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Observation 94c8d1f9-f04e-4f36-bb2e-589ed3deed78 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 7

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Observation 2f56a8d4-6bf5-46a7-91d2-b2f0a5f64961 · outbound

This paper cites Hoda ˇn, D.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Hoda ˇn, D

Reference 8

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Observation b91e0206-1273-42ad-89c9-0852eb7049e1 · outbound

This paper cites Okorn, M.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Okorn, M

Reference 9

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Observation 2350c32d-77ab-4c53-86a3-b458169ab4c9 · outbound

This paper cites Yang and M.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Yang and M

Reference 10

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Observation fffbc577-0bc8-4d90-bdc5-7680955dd326 · outbound

This paper cites Tang, J-B.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Tang, J-B

Reference 11

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Observation 52ce03c5-1d76-44f7-8aaa-e645d84e03a8 · outbound

This paper cites Pólik and T.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Pólik and T

Reference 12

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Observation b02ac2ef-2c65-4c4c-add3-5b6f3c6e5cf5 · outbound

This paper cites Brachmann, A.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Brachmann, A

Reference 13

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Observation b949de7b-cc4d-437d-96b1-9aecc1eea9eb · outbound

This paper cites Xiang, T.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Xiang, T

Reference 14

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Observation c94bd531-bccd-479f-8cf7-f22ac720a2a6 · outbound

This paper cites Shaikewitz, S.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Shaikewitz, S

Reference 15

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Observation d2c9afd8-bf9b-4ac0-adb8-05c5a472304c · outbound

This paper cites Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation

Reference 16

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Observation 82e2c672-ae34-4c75-84de-e1fd2cc45efa · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 17

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Observation bc9de15c-fa72-44f3-b74b-ff4afea6b551 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 18

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Observation 8480809e-472f-4ab2-85dc-6cbd185ae296 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 19

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Observation 2cb1482e-6e0d-4b1c-858b-b99b49dc06e3 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 20

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Observation 8a4cb725-2088-411b-bb21-b59179b10d44 · outbound

This paper cites Shafer and V.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Shafer and V

Reference 21

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Observation 4711c666-52c8-4d97-a128-0b52d70c9191 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 22

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Observation e12518a3-6a59-4eab-91d7-0ba87034252f · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 23

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Observation 3262d9bc-1113-454c-9b13-0683b1c5b910 · outbound

This paper cites Ben-Tal and A.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Ben-Tal and A

Reference 24

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Observation 824e135d-3429-41ba-8516-a8167766b292 · outbound

This paper cites Nie and J.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Nie and J

Reference 25

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Observation ec32d2e9-b2ea-434d-ac3d-da7637546feb · outbound

This paper cites Casini, A.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Casini, A

Reference 26

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Observation b8565742-bed3-4af4-8a51-21cf6dcc7fda · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 27

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Observation 16caa986-d238-4432-84a6-f26470798208 · outbound

This paper cites Theoretical Foundations of Conformal Prediction.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Theoretical Foundations of Conformal Prediction

Reference 28

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Observation 7f58efd8-d24e-4e7b-95fe-fa0384ff77b7 · outbound

This paper cites Terzakis and M.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Terzakis and M

Reference 29

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Observation 7bac61e5-c5fb-4ee2-9905-b4f62c9061e6 · outbound

This paper cites Lessard,The S-Lemma.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Lessard,The S-Lemma

Reference 30

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Observation c2b81c98-fec9-48d3-ba47-f827c70845ef · outbound

This paper cites Boyd and L.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Boyd and L

Reference 31

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Observation ba6b9746-cc77-436a-9642-78721b160607 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 32

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Observation d61910ae-43b7-44f3-a26f-bc714afe0ccd · outbound

This paper cites Version 11.0.30(2025).

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Version 11.0.30(2025)

Reference 33

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Observation 3c7e6878-ba73-443e-9d4e-29d2c261e39b · outbound

This paper cites Nie,Optimality conditions and finite convergence of Lasserre’s hierarchy, Mathematical programming,146(1-2) (2014) 97–121.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Nie,Optimality conditions and finite convergence of Lasserre’s hierarchy, Mathematical programming,146(1-2) (2014) 97–121

Reference 34

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Observation 1fde9eb1-d7e0-4800-8413-2c6559908cd9 · outbound

This paper cites CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization

Reference 35

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Observation 777d9788-4662-4607-8c4f-b78f3d797468 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 36

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Observation 968f0abb-dcb7-417a-b31d-861bcb152838 · outbound

This paper cites Barfoot,State Estimation for Robotics, (2017) Cambridge University Press.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Barfoot,State Estimation for Robotics, (2017) Cambridge University Press

Reference 37

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Observation 752521fe-a872-427a-bea4-493988acc999 · outbound

This paper cites Hoda ˇn, M.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Hoda ˇn, M

Reference 38

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Observation 4e321ea8-d5e7-439e-bc3d-ffb62e90aa94 · outbound

This paper cites Semantic keypoint-based pose estimation from single RGB frames.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Semantic keypoint-based pose estimation from single RGB frames

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a4ea1029-7dba-4362-83b5-823f0012ed09 · outbound

This paper cites Hoda ˇn, F.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Hoda ˇn, F

Reference 40

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Observation 05e5ac9c-228b-432c-81ef-274a98e7a654 · outbound

This paper cites an unresolved cited work.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Unresolved cited work

Reference 41

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Observation 82bfce67-c5f2-49d7-8609-d8259cbaf7a7 · outbound

This paper cites Denninger, D.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Denninger, D

Reference 42

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Observation 8bb6240b-9b1d-4ab4-a0c9-f0a3ea18427a · outbound

This paper cites Yang and L.

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds Yang and L

Reference 43

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unresolved
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Source-reported events for the cited work

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Pith citing papers

Observation 02e03173-3276-4e41-9485-3e946427327a · inbound

Provably Guaranteed Polytopic Uncertainty Quantification for SLAM cites this paper.

Provably Guaranteed Polytopic Uncertainty Quantification for SLAM Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds

Reference 22

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verified exact
arxiv_id, observed 2026-08-04T02:39:46.582683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7fee80af-6529-435c-adad-83cb31bc692a · inbound

Detecting Pose Estimation Failures via Keypoint Self-Consistency cites this paper.

Detecting Pose Estimation Failures via Keypoint Self-Consistency Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds

Reference 43

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