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Paper Citation Record · LEDGER

UniPose9D: Universal Category-Agnostic Object Pose Estimation

As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2607.09985.

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

pith.paper-citation-record.v1
2607.09985 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T01:15:46.555348Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved46
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

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Outbound references

Observation 0c0d866e-24ab-4ae8-acbc-2dc9ce0733d6 · outbound

This paper cites Gaussian mixture flow matching models.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Gaussian mixture flow matching models

Reference 1

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:f6b2b006ef2de4b7a9d917941a60715d3aaff54d00d58eab8d3a45cadaadebb1

Observation 280b5381-9231-4e8d-b63b-e5e12ead29fb · outbound

This paper cites Sgpa: Structure-guided prior adaptation for category-level 6d object pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Sgpa: Structure-guided prior adaptation for category-level 6d object pose estimation

Reference 2

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:b241c0451b7d9b5c0232b88dac988b4cfc31573fa259f351bd85e2b5ec6a735d

Observation a2cc22d2-44ca-44f9-aa96-c334b4f7c0a6 · outbound

This paper cites Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism

Reference 3

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Observation 3d1cc0d5-7fa4-4578-bb3e-a77620e1e2dc · outbound

This paper cites Secondpose: Se (3)-consistent dual-stream feature fusion for category-level pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Secondpose: Se (3)-consistent dual-stream feature fusion for category-level pose estimation

Reference 4

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:1fa98ed109427e0b0c96ba689b5452583503691af5cd9d2ce45c01f22618c94e

Observation 1e93d58a-9d5d-4639-80da-2227f0d4fa1d · outbound

This paper cites Gpv-pose: Category-level object pose estimation via geometry-guided point-wise voting.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Gpv-pose: Category-level object pose estimation via geometry-guided point-wise voting

Reference 5

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:eee8dd98e29d4298c6f28ae7157f550d4742bc8d7ce6e44288c29aaed5e8ee44

Observation da7d50f2-9ec3-47ca-ba71-291c354a4e4a · outbound

This paper cites Model globally, match locally: Efficient and robust 3d object recognition.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Model globally, match locally: Efficient and robust 3d object recognition

Reference 6

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:c0719c190ad6e7ac3c076a37b727ad3d0369f54b7f47a21e3b0d3c0b9bba1aec

Observation 2983e8fa-3e07-406b-a088-64b5235c60a6 · outbound

This paper cites an unresolved cited work.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Unresolved cited work

Reference 7

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:8f1a2c93a85ac6bfbcf9fd8f285936ce86ce6894ba2dfe66fb59447fbd76c199

Observation 9884e52f-91c0-4565-a4ec-b4937db2fc9b · outbound

This paper cites Surfemb: Dense and continuous correspondence distributions for object pose estimation with learnt surface embeddings.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Surfemb: Dense and continuous correspondence distributions for object pose estimation with learnt surface embeddings

Reference 8

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:534b640cebc5912e3de0c0970a8fa8dc949ed9e7fc618cf42eda4312ca59d0c7

Observation af60de56-bcc1-4d58-a34e-b8c3172bac54 · outbound

This paper cites Onepose++: Keypoint- free one-shot object pose estimation without cad models.Advances in Neural Information Processing Systems, 35:35103–35115, 2022.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Onepose++: Keypoint- free one-shot object pose estimation without cad models.Advances in Neural Information Processing Systems, 35:35103–35115, 2022

Reference 9

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:ffe9810c19336e9c115da76617b857410e35ec8415d4eebd094287a24e0eb574

Observation 4e813baa-af3f-4e97-a87f-e1a63ed6ee68 · outbound

This paper cites Fs6d: Few-shot 6d pose estimation of novel objects.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Fs6d: Few-shot 6d pose estimation of novel objects

Reference 10

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:e2698814b8accf5082a64de2bac3cda34ea3b272208ef6cc9d37873ae81105c0

Observation 461c8533-1639-4e67-8df1-71dffa7215ea · outbound

This paper cites Housecat6d-a large-scale multi-modal category level 6d object perception dataset with household objects in realistic scenarios.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Housecat6d-a large-scale multi-modal category level 6d object perception dataset with household objects in realistic scenarios

Reference 11

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:b6216e0ab73503b5af9526b20a2b802e70d192bd8f0a59cbb26f2526fc360def

Observation 8ed9961c-8375-4cf0-a046-708fffd8ed1a · outbound

This paper cites Cosypose: Consistent multi-view multi- object 6d pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Cosypose: Consistent multi-view multi- object 6d pose estimation

Reference 12

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:01fb475436d728bb71b943cb4617ff780f31ee3361ab0607e84be46e454b0459

Observation 0f8f1242-5bfd-41ef-bb75-ad47dc0c0188 · outbound

This paper cites A purely algebraic justification of the kabsch- umeyama algorithm.Journal of Research of the National Institute of Standards and Technology, 124: 124028, 2019.

UniPose9D: Universal Category-Agnostic Object Pose Estimation A purely algebraic justification of the kabsch- umeyama algorithm.Journal of Research of the National Institute of Standards and Technology, 124: 124028, 2019

Reference 13

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:b9dc5e019d8a535ef3f900d0773336d09287d8ec61a0191477bccccfed297d01

Observation de10fce6-1a31-4c32-b17c-77a965ca3e09 · outbound

This paper cites Any6D: Model-free 6d pose estimation of novel objects.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Any6D: Model-free 6d pose estimation of novel objects

Reference 14

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:7d2edaec61ee13dda9ce8075a8aaf5f40f5888258ac7a59ade9e70148d306f5a

Observation d90e9759-422b-4392-8a03-b01736952c29 · outbound

This paper cites Sar-net: Shape alignment and recovery network for category-level 6d object pose and size estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Sar-net: Shape alignment and recovery network for category-level 6d object pose and size estimation

Reference 15

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Observation 4e380aa1-e555-4277-9f75-fd9b3bb1b9ea · outbound

This paper cites an unresolved cited work.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Unresolved cited work

Reference 16

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:bd4ff61323ef0aa8dd1f977dd399d5edc87c4e7b72b673b5d7c953c7be3e868a

Observation 24a4744b-bb35-40bf-84e0-cf4db888492d · outbound

This paper cites Dualposenet: Category-level 6d object pose and size estimation using dual pose network with refined learning of pose consistency.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Dualposenet: Category-level 6d object pose and size estimation using dual pose network with refined learning of pose consistency

Reference 17

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:ff377b86260158843a6269d2a7a7f76388d255114e7cdf6cde26fa8466d02083

Observation 0631daf4-8d22-4764-be59-f4b534073643 · outbound

This paper cites Vi-net: Boosting category-level 6d object pose estimation via learning decoupled rotations on the spherical representations.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Vi-net: Boosting category-level 6d object pose estimation via learning decoupled rotations on the spherical representations

Reference 18

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Observation eefe7c53-7a53-47d9-9933-3188b88f9e65 · outbound

This paper cites Instance-adaptive and geometric-aware keypoint learning for category-level 6d object pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Instance-adaptive and geometric-aware keypoint learning for category-level 6d object pose estimation

Reference 19

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Observation f83e6b9c-afc7-4a3b-8982-d2b97dcbe3b7 · outbound

This paper cites Ist-net: Prior-free category-level pose estimation with implicit space transformation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Ist-net: Prior-free category-level pose estimation with implicit space transformation

Reference 20

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:3ad0727ffa78189c6e572d645fc12b3ef127298b0bc427e23225745be68e6243

Observation 78cf5c6e-c168-40c7-91d8-01f5dc8f4e64 · outbound

This paper cites Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Gen6d: Generalizable model-free 6-dof object pose estimation from rgb images

Reference 21

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:c4d17ecc5869c73a8bd37e6738814040971606d7292a6e106fc01853b87027c9

Observation 8e2d6dd7-0495-45d2-be74-514fe3fec754 · outbound

This paper cites Pose estimation for augmented reality: a hands-on survey.IEEE transactions on visualization and computer graphics, 22(12):2633–2651, 2015.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Pose estimation for augmented reality: a hands-on survey.IEEE transactions on visualization and computer graphics, 22(12):2633–2651, 2015

Reference 22

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Observation 1bd5368c-45dc-40f9-96d2-2da2ec929779 · outbound

This paper cites Project tango.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Project tango

Reference 23

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:563a29754ba6603abd370ac7f43f89f43a11e7fbcaff8ea3232160dc45778f8e

Observation 88b53aab-6119-4690-8c20-9b00324828f2 · outbound

This paper cites Nope: Novel object pose estimation from a single image.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Nope: Novel object pose estimation from a single image

Reference 24

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Observation 060633db-c13c-412e-9922-5038f22dfe31 · outbound

This paper cites Gigapose: Fast and robust novel object pose estimation via one correspondence.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Gigapose: Fast and robust novel object pose estimation via one correspondence

Reference 25

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Observation 025d0dde-d8ae-4baf-abb8-efffd3b09e29 · outbound

This paper cites an unresolved cited work.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Unresolved cited work

Reference 26

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Observation 8e4cb96f-a721-47ba-a175-1d410c947112 · outbound

This paper cites Qi, Hao Su, Kaichun Mo, and Leonidas J.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Qi, Hao Su, Kaichun Mo, and Leonidas J

Reference 27

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:182314aac77754203ef33bf5ef9c94d55843c2ed2c342767347cd6ad7965d00b

Observation 9e69eb34-f3ad-41a4-9123-b9a8e57c77ec · outbound

This paper cites Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Maskfusion: Real-time recognition, tracking and reconstruction of multiple moving objects

Reference 28

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Observation a3311886-2e99-4abc-9726-fa8c1cecec57 · outbound

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

UniPose9D: Universal Category-Agnostic Object Pose Estimation Loftr: Detector-free local feature matching with transformers

Reference 29

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:d2d57463d0aae0e633908127edc9a0ebdbd9b6ea6197e6ba04a5ef273a5e507c

Observation bb9c9e1a-89aa-437a-9de9-965e05dbeef5 · outbound

This paper cites Onepose: One-shot object pose estimation without cad models.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Onepose: One-shot object pose estimation without cad models

Reference 30

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Observation 5e4c99f5-0cc2-4b45-841a-1e4ad46c4198 · outbound

This paper cites 6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark.

UniPose9D: Universal Category-Agnostic Object Pose Estimation 6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark

Reference 31

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Observation d385ca6d-25c5-4f2e-87ed-459eb30c566e · outbound

This paper cites GDR-Net: Geometry-guided direct regression network for monocular 6d object pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation GDR-Net: Geometry-guided direct regression network for monocular 6d object pose estimation

Reference 32

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Observation dce57aab-bd2f-4b2e-a8b3-2aa6d6b4f4a1 · outbound

This paper cites Normal- ized object coordinate space for category-level 6d object pose and size estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Normal- ized object coordinate space for category-level 6d object pose and size estimation

Reference 33

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Observation eabe953a-f97f-466c-8872-13b3e075953e · outbound

This paper cites MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details.

UniPose9D: Universal Category-Agnostic Object Pose Estimation MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

Reference 34

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Observation 6c92e4fd-512b-4dd8-8b10-ed58a976c99e · outbound

This paper cites Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models

Reference 35

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Observation 966e7707-a82e-4af5-970b-bff23a7fa930 · outbound

This paper cites You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration.

UniPose9D: Universal Category-Agnostic Object Pose Estimation You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

Reference 36

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Observation f36e9df1-e1f1-4a7b-ad86-3a036303f931 · outbound

This paper cites Foundationpose: Unified 6d pose estimation and tracking of novel objects.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Foundationpose: Unified 6d pose estimation and tracking of novel objects

Reference 37

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:3458a4ec74f868401d43f7e071df392f9df75c049805bc2270e646eeb107c042

Observation 6dba680c-13bf-4b10-a4fc-263bbd3b1a12 · outbound

This paper cites Segicp: Integrated deep semantic segmentation and pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Segicp: Integrated deep semantic segmentation and pose estimation

Reference 38

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:24151b2af8318374a7cab9a6593a191997a36539184cd5ccd35096da0b97f723

Observation 78dd0f28-3c76-4ff5-9717-deb012d73f44 · outbound

This paper cites PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes.

UniPose9D: Universal Category-Agnostic Object Pose Estimation PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Reference 39

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:d900140309058456feea4fe86f0d877282850eb34e9b0f6e2f6d2d301373bc1e

Observation 3824c49b-099a-4f2d-8c4a-b4f35c22ad1e · outbound

This paper cites CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation

Reference 40

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:b7b36a1fa251638b226e370056960ac54a0bc4d9c9c86211b3ffd3ece5b12f67

Observation 78ae2ad5-bbc7-48fb-bb7e-b4c190c1bff3 · outbound

This paper cites Cppf: Towards robust category-level 9d pose estimation in the wild.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Cppf: Towards robust category-level 9d pose estimation in the wild

Reference 41

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:3e9d9842a64b6c0df227d9cee524adb0a51781f36518268985404102a41097e9

Observation 6a796680-a3f6-4364-9574-c59500630a8f · outbound

This paper cites Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning

Reference 42

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:e5916c77be7b06ac84d98a3c3ca0cdccfcb8c0b83a9fd4c11800e9573a2cfd23

Observation 2cac0ce8-4f6f-4d5d-9d28-b2b1e929cb90 · outbound

This paper cites Pace: A large-scale dataset with pose annotations in cluttered environments.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Pace: A large-scale dataset with pose annotations in cluttered environments

Reference 43

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:0f1fd65c67e20fdd9495a448f4064cf8a4e773fad7c51428f2c0c6351ae8b9a0

Observation 75cd5934-17c9-442d-a040-ec449a0a00f5 · outbound

This paper cites Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge

Reference 44

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:e24b6f0cff6bcfa5439bb2f2516b9399755b764013444f451b8440b6548ed1b9

Observation b179548e-7d08-491d-872c-a983ad6507a5 · outbound

This paper cites GenPose: Generative Category-level Object Pose Estimation via Diffusion Models.

UniPose9D: Universal Category-Agnostic Object Pose Estimation GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

Reference 45

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:c2ff2b0791c5100a2552e7940cdf3b71cb9c8d53a158ed79e22e9186b04fef5d

Observation 63333faf-e9c1-4e1f-aa9f-01228bf6a189 · outbound

This paper cites Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

Reference 46

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:5723191f145ce9887854d6e2c2d65a0b548439ae97ba6e90a3be76be19f802e7

Observation d665dedc-d623-47e5-a08b-fc977baafb29 · outbound

This paper cites Hs-pose: Hybrid scope feature extraction for category-level object pose estimation.

UniPose9D: Universal Category-Agnostic Object Pose Estimation Hs-pose: Hybrid scope feature extraction for category-level object pose estimation

Reference 47

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source=pdf_text observed=2026-07-14T01:15:46.555348Z digest=sha256:e849ead65e104dbfc1c05975fc1d518c941e520b5687c39e804fa3a976739043

Pith citing papers

No inbound Pith citation observations are available.