Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation | Paper Citation Record

Observation 50a2aa24-9389-46fc-b4f1-91ca101ff8f5 · outbound

This paper cites Pose guided rgbd feature learning for 3d object pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Pose guided rgbd feature learning for 3d object pose estimation

Reference 1

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Observation c184cd61-3828-4474-a499-ba48e0647625 · outbound

This paper cites Ove6d: Object viewpoint encoding for depth-based 6d object pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Ove6d: Object viewpoint encoding for depth-based 6d object pose estimation

Reference 2

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Observation 5ea4103e-0329-4610-b44b-3a1d21044408 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation ShapeNet: An Information-Rich 3D Model Repository

Reference 3

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Observation e29b778e-8c78-42d6-8488-20db57fafc26 · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Sgpa: Structure-guided prior adapta- tion for category-level 6d object pose estimation

Reference 4

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Observation d9ffd5e9-7b9c-4e56-9106-b97cbd8b2c93 · outbound

This paper cites Stereopose: Category-level 6d transparent object pose estimation from stereo images via back-view nocs.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Stereopose: Category-level 6d transparent object pose estimation from stereo images via back-view nocs

Reference 5

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Observation c529053a-5a8b-45f6-9a74-28440a0687b6 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation A simple framework for contrastive learning of visual representations

Reference 6

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Observation c04237e0-2447-4147-93dc-7eb69b56e853 · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism

Reference 7

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Observation eb085693-4a38-42eb-ac6a-f2ba69376ee4 · outbound

This paper cites Category level object pose estimation via neu- ral analysis-by-synthesis.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Category level object pose estimation via neu- ral analysis-by-synthesis

Reference 8

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Observation 48a6f56a-af54-4946-b7c9-edbf9a3667e5 · outbound

This paper cites Holistic++ scene understanding: Single-view 3d holistic scene parsing and human pose es- timation with human-object interaction and physical com- monsense.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Holistic++ scene understanding: Single-view 3d holistic scene parsing and human pose es- timation with human-object interaction and physical com- monsense

Reference 9

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Observation 5343acf8-8771-4a6d-b98a-e8e62c973625 · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Secondpose: Se (3)- consistent dual-stream feature fusion for category-level pose estimation

Reference 10

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Observation d34532ad-2244-4636-a7e6-b86c4fd87e11 · outbound

This paper cites Revisit- ing fully convolutional geometric features for object 6d pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Revisit- ing fully convolutional geometric features for object 6d pose estimation

Reference 11

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Observation 46712fd2-1339-4f9d-a4ed-31d8efb37c25 · outbound

This paper cites Self-supervised 6d object pose estimation for robot manipulation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Self-supervised 6d object pose estimation for robot manipulation

Reference 12

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Observation f723f13c-aa96-4955-ba49-277c4c971d80 · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Gpv-pose: Category-level object pose estimation via geometry-guided point-wise voting

Reference 13

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Observation 7117b07f-d567-4914-8851-1dcc7c00eb86 · outbound

This paper cites Ppr-net: point-wise pose regression network for instance segmentation and 6d pose es- timation in bin-picking scenarios.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Ppr-net: point-wise pose regression network for instance segmentation and 6d pose es- timation in bin-picking scenarios

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:13.511750Z

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Observation 97f33fa5-2ea4-4885-bc4d-fca2a8d950a1 · outbound

This paper cites Notes on Noise Contrastive Estimation and Negative Sampling.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Notes on Noise Contrastive Estimation and Negative Sampling

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:42:05.598535Z

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Observation d1221ca5-5c6e-465b-a43a-f9cbed814ca5 · outbound

This paper cites Fully convolutional ge- ometric features for category-level object alignment.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Fully convolutional ge- ometric features for category-level object alignment

Reference 16

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Observation 02d0e20b-43d3-4df4-b142-85578b737bda · outbound

This paper cites 6d object pose regression via supervised learning on point clouds.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation 6d object pose regression via supervised learning on point clouds

Reference 17

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Observation 52f77d1d-4139-4752-9994-3f86bad1b310 · outbound

This paper cites Mask r-cnn.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Mask r-cnn

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:13.042301Z

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Observation 64b404b8-603f-4f90-b0c6-3a8cf36c8d31 · outbound

This paper cites Ffb6d: A full flow bidirectional fusion network for 6d pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Ffb6d: A full flow bidirectional fusion network for 6d pose estimation

Reference 19

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Observation 33393427-be32-4673-99aa-3adf105b572a · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 20

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Observation 02585d09-fc1c-425a-a68b-1e8355547eab · outbound

This paper cites Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,

Reference 21

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Observation cbf1e7df-fd63-4f44-97ac-fe7b973ca53b · outbound

This paper cites Con- trastive representation learning: A framework and review.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Con- trastive representation learning: A framework and review

Reference 22

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Observation 9b1b39f7-3873-48f4-8fb7-4e583660c43f · outbound

This paper cites Category-level metric scale object shape and pose estimation.IEEE Robotics and Automation Letters, 6(4): 8575–8582, 2021.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Category-level metric scale object shape and pose estimation.IEEE Robotics and Automation Letters, 6(4): 8575–8582, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:12.745086Z

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Observation 2e9622ee-d01e-428f-b839-2a8caeb156d8 · outbound

This paper cites Generative category-level shape and pose estimation with semantic primitives.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Generative category-level shape and pose estimation with semantic primitives

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:12.663669Z

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Observation 525ef5cd-26a3-4263-a1af-cf1816807594 · outbound

This paper cites Depth-based 6dof object pose estimation using swin transformer.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Depth-based 6dof object pose estimation using swin transformer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:12.427527Z

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Observation a607fdaa-200e-462d-a067-86301577441d · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Sar-net: Shape align- ment and recovery network for category-level 6d object pose and size estimation

Reference 26

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Observation 8de28cd1-0c22-4ee9-8c3b-f19bd1720a1f · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Dualposenet: Category-level 6d object pose and size estimation using dual pose network with re- fined learning of pose consistency

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:11.973396Z

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Observation b41cf352-4fa1-44be-9fd5-7000b8ee3826 · outbound

This paper cites Category-level 6d object pose and size estimation using self- supervised deep prior deformation networks.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Category-level 6d object pose and size estimation using self- supervised deep prior deformation networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:11.750505Z

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Observation 498554af-3b4d-449d-9a1b-6613e016b56e · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Instance-adaptive and geometric-aware keypoint learning for category-level 6d object pose estimation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:11.511309Z

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Observation 8b1ed5bb-7605-4554-9ce4-823ab18d87c3 · outbound

This paper cites Cli- pose: Category-level object pose estimation with pre-trained vision-language knowledge.IEEE Transactions on Circuits and Systems for Video Technology, 2024.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Cli- pose: Category-level object pose estimation with pre-trained vision-language knowledge.IEEE Transactions on Circuits and Systems for Video Technology, 2024

Reference 30

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Observation 8c9376e9-9d91-4499-98ce-f1ae983e103a · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Ist-net: Prior-free category-level pose estimation with im- plicit space transformation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:11.108944Z

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Observation aa59759d-bbba-4385-9f9d-741e1ad87384 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 32

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Observation 2268c9c5-0ac0-4a17-a962-f2a1551c69d9 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Representation Learning with Contrastive Predictive Coding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T18:42:03.081237Z

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Observation e64294b4-3323-4092-aeee-2204fb089eaa · outbound

This paper cites Pvnet: Pixel-wise voting network for 6dof pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Pvnet: Pixel-wise voting network for 6dof pose estimation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:10.812770Z

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Observation a341528a-0295-447d-bf27-cd6d217898d1 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017

Reference 35

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Observation 408c6765-56e5-4b40-b720-340539a2a44b · outbound

This paper cites Maximum likelihood training of score-based diffusion mod- els.Advances in neural information processing systems, 34: 1415–1428, 2021.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Maximum likelihood training of score-based diffusion mod- els.Advances in neural information processing systems, 34: 1415–1428, 2021

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:10.239122Z

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Observation 7e029d7a-21df-4f0a-94fd-a36b87c3ac03 · outbound

This paper cites Deep multi-state object pose estimation for augmented reality assembly.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Deep multi-state object pose estimation for augmented reality assembly

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:09.923756Z

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Observation 4c47cbf1-1582-40d2-b1c2-b4b82edb97c6 · outbound

This paper cites Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose es- timation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose es- timation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:09.617288Z

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Observation abe653a0-7d2b-4d40-bd90-e4d182c284ba · outbound

This paper cites Shape prior deformation for categorical 6d object pose and size estima- tion.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Shape prior deformation for categorical 6d object pose and size estima- tion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:09.317052Z

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Observation 52394088-d40b-402c-acb6-fb08b3307f4a · outbound

This paper cites Shape prior deformation for categorical 6d object pose and size es- timation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Shape prior deformation for categorical 6d object pose and size es- timation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:09.118940Z

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Observation f6de3c4a-5bab-4ad2-bd91-0dcbfd8094c2 · outbound

This paper cites Least-squares estimation of transforma- tion parameters between two point patterns.IEEE Transac- tions on Pattern Analysis & Machine Intelligence, 13(04): 376–380, 1991.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Least-squares estimation of transforma- tion parameters between two point patterns.IEEE Transac- tions on Pattern Analysis & Machine Intelligence, 13(04): 376–380, 1991

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:08.905681Z

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Observation 407dfacd-062d-41f3-9525-6f07c411cc7c · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Normalized object coordinate space for category-level 6d object pose and size estimation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:08.666524Z

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Observation 9b511ac2-4b75-4d88-a1d3-9f7d0b8027d9 · outbound

This paper cites Attention-guided rgb-d fusion network for category-level 6d object pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Attention-guided rgb-d fusion network for category-level 6d object pose estimation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:08.476035Z

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Observation 496cf480-4ba4-4561-a175-5cc6ed7df482 · outbound

This paper cites Category-level 6d object pose estimation via cascaded relation and recurrent recon- struction networks.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Category-level 6d object pose estimation via cascaded relation and recurrent recon- struction networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:08.284728Z

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Observation 9e4e4227-dcc3-46df-a80a-7b900557515f · outbound

This paper cites Contrastive learning based hybrid networks for long- tailed image classification.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Contrastive learning based hybrid networks for long- tailed image classification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:08.043803Z

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Observation 5d579aa1-ca5d-4308-b981-7380a5b79345 · outbound

This paper cites Query6dof: Learning sparse queries as implicit shape prior for category-level 6dof pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Query6dof: Learning sparse queries as implicit shape prior for category-level 6dof pose estimation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:07.797494Z

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Observation 07ddead6-9e7a-4e58-b651-eb5d1a144a52 · outbound

This paper cites Distance met- ric learning for large margin nearest neighbor classification.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Distance met- ric learning for large margin nearest neighbor classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:07.620324Z

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Observation 1cd8bce9-9116-4641-9d96-6d2f95d0c8cd · outbound

This paper cites Learning descriptors for object recognition and 3d pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Learning descriptors for object recognition and 3d pose estimation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:07.422154Z

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Observation 6d43757d-a0d5-4f64-85b7-74ce1da1078d · outbound

This paper cites 6d-vnet: End-to-end 6-dof vehicle pose es- timation from monocular rgb images.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation 6d-vnet: End-to-end 6-dof vehicle pose es- timation from monocular rgb images

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:07.205605Z

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Observation 7d558b53-06d9-4ae4-bfe3-6af78879c081 · outbound

This paper cites Posecontrast: Class-agnostic object viewpoint estimation in the wild with pose-aware contrastive learning.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Posecontrast: Class-agnostic object viewpoint estimation in the wild with pose-aware contrastive learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:07.022968Z

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Observation 62facf35-1097-4e8f-8e8e-42606df14f4c · outbound

This paper cites 6DoF Object Pose Estimation via Differentiable Proxy Voting Loss.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation 6DoF Object Pose Estimation via Differentiable Proxy Voting Loss

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:42:05.406467Z

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Observation 0ea5ada8-3958-4ea5-95fb-2586b48c0074 · outbound

This paper cites 3d object instance recognition and pose estimation using triplet loss with dynamic margin.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation 3d object instance recognition and pose estimation using triplet loss with dynamic margin

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:06.798099Z

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Observation cd236dce-1541-4d7e-8136-3766f769eecd · outbound

This paper cites Rank-n-contrast: learning continuous representa- tions for regression.Advances in Neural Information Pro- cessing Systems, 36, 2024.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Rank-n-contrast: learning continuous representa- tions for regression.Advances in Neural Information Pro- cessing Systems, 36, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:06.595750Z

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Observation 48dfc036-44a8-4b5b-8608-d77582e0964b · outbound

This paper cites Rbp-pose: Residual bounding box projection for category-level pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Rbp-pose: Residual bounding box projection for category-level pose estimation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:06.435973Z

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Observation fbb74d04-1c7c-4315-98f5-606ff8d12234 · outbound

This paper cites Ssp-pose: Symmetry-aware shape prior deformation for direct category-level object pose estimation.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Ssp-pose: Symmetry-aware shape prior deformation for direct category-level object pose estimation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:06.099458Z

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Observation 03f7a778-490a-40e9-a839-90bdcca7c862 · outbound

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

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Hs-pose: Hybrid scope feature extraction for category-level object pose estimation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:05.932640Z

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Observation fa102f07-04b4-45f8-a442-f48eb3f15178 · outbound

This paper cites Weakly super- vised contrastive learning.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Weakly super- vised contrastive learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:42:05.749483Z

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Observation d0c32372-8b4e-4897-a72b-7e8a94ba5d56 · outbound

This paper cites an unresolved cited work.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation Unresolved cited work

Reference 672

Resolution: parse uncertain; raw_fallback, observed 2026-08-05T18:42:06.290868Z

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