mlsanityData to 14 Oct 2026

GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation

arXiv 2503.15110 ↗First posted 19 Mar 2025cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from instance-level tasks has demonstrated strong performance. However, we argue that the NOCS map is an inadequate intermediate representation for geometry-guided pose regression method, as its many-to-one correspondence with category-level pose introduces redundant instance-specific information, resulting in suboptimal results. This paper identifies the intra-class variation problem inherent in pose regression based solely on the NOCS map and proposes the Intra-class Variation-Free Consensus (IVFC) map, a novel coordinate representation generated from the category-level consensus model. By leveraging the complementary strengths of the NOCS map and the IVFC map, we introduce GIVEPose, a framework that implements Gradual Intra-class Variation Elimination for category-level object pose estimation. Extensive evaluations on both synthetic and real-world datasets demonstrate that GIVEPose significantly outperforms existing state-of-the-art RGB-based approaches, achieving substantial improvements in category-level object pose estimation. Our code is available at https://github.com/ziqin-h/GIVEPose.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 66 of its 85 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

66 of 85 references.

References · 66

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Mask R-CNNcited by 7,277
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  5. 2021Vision Transformers for Dense Predictioncited by 1,803
  6. 2018Open3D: A Modern Library for 3D Data Processingcited by 721
  7. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  8. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 482
  9. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  10. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  11. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  12. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
38 more
  1. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  2. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  3. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  4. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 161
  5. 2020EPOS: Estimating 6D Pose of Objects with Symmetriescited by 148
  6. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  7. 2018ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shapecited by 123
  8. 2021FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanismcited by 121
  9. 2022ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimationcited by 116
  10. 2020HybridPose: 6D Object Pose Estimation under Hybrid Representationscited by 107
  11. 2021DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistencycited by 103
  12. 2021SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddingscited by 95
  13. 2022GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Votingcited by 94
  14. 2019Single-Stage 6D Object Pose Estimationcited by 88
  15. 2021SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimationcited by 88
  16. 2022You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstrationcited by 81
  17. 2022Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networkscited by 74
  18. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 72
  19. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 70
  20. 2021Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networkscited by 69
  21. 2018Explaining the Ambiguity of Object Detection and 6D Pose From Visual Datacited by 68
  22. 2024BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objectscited by 60
  23. 2022Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Datasetcited by 59
  24. 2022Zero-Shot Category-Level Object Pose Estimationcited by 57
  25. 2023SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimationcited by 53
  26. 2022RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimationcited by 43
  27. 2024Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose Estimationcited by 43
  28. 2021Joint Learning of 3D Shape Retrieval and Deformationcited by 42
  29. 2021Category-Level Metric Scale Object Shape and Pose Estimationcited by 41
  30. 2022SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimationcited by 41
  31. 2023IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformationcited by 41
  32. 2023VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representationscited by 41
  33. 2022Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimationcited by 34
  34. 2019On Object Symmetries and 6D Pose Estimation from Imagescited by 33
  35. 2022Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Imagecited by 30
  36. 2024DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimationcited by 25
  37. 2022MonoGraspNet: 6-DoF Grasping with a Single RGB Imagecited by 24
  38. 2023Context Disentangling and Prototype Inheriting for Robust Visual Groundingcited by 23

50 most cited of 66 All 66 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025You Only Pose Once: A Minimalist's Detection Transformer for Monocular RGB Category-level 9D Multi-Object Pose Estimationcited by 3
  2. 2026Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimationcited by 1
  3. 2026Object Pose Transformer: Unifying Unseen Object Pose Estimationcited by 0
  4. 2026Emergence of a Shared Canonical Object Frame from In-the-Wild Videoscited by 0
  5. 2026GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Pickingcited by 0
  6. 2026SchemaPose: RGB-Based Category-Level Pose Estimation with Parametric Category Schemacited by 0