mlsanityData to 14 Oct 2026

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

arXiv 1901.02970 ↗First posted 9 Jan 2019cs.CVcited by 495

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new context-aware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.

Key facts. Cited by 495 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jan 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 59; 2022: 102; 2023: 91; 2024: 88; 2025: 83; 2026: 72.

495 papers.

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  1. 2021Neural Fields in Visual Computing and Beyondcited by 540
  2. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 429
  3. 2022HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interactioncited by 327
  4. 2021DexYCB: A Benchmark for Capturing Hand Grasping of Objectscited by 325
  5. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 305
  6. 2022MegaPose: 6D Pose Estimation of Novel Objects via Render & Comparecited by 201
  7. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 182
  8. 2022OnePose: One-Shot Object Pose Estimation without CAD Modelscited by 157
  9. 2021GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimatorcited by 148
  10. 2023BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objectscited by 148
  11. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  12. 2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 139
38 more
  1. 2024ShapeLLM: Universal 3D Object Understanding for Embodied Interactioncited by 137
  2. 2023SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3Dcited by 134
  3. 2021FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanismcited by 121
  4. 2022Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Imagescited by 121
  5. 2023Unsupervised Semantic Correspondence Using Stable Diffusioncited by 120
  6. 2023SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimationcited by 117
  7. 2022ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimationcited by 116
  8. 2023OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Modelscited by 115
  9. 2024RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videoscited by 115
  10. 2023GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondencecited by 114
  11. 2021DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistencycited by 103
  12. 2022GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Votingcited by 94
  13. 2021BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Modelscited by 87
  14. 2025Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assetscited by 87
  15. 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 84
  16. 2021Geometry-based Distance Decomposition for Monocular 3D Object Detectioncited by 84
  17. 2021MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagationcited by 82
  18. 2021MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matchingcited by 82
  19. 2022AKB-48: A Real-World Articulated Object Knowledge Basecited by 82
  20. 2022You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstrationcited by 81
  21. 2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
  22. 2022OSOP: A Multi-Stage One Shot Object Pose Estimation Frameworkcited by 81
  23. 2023EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimationcited by 79
  24. 2022Full-Body Articulated Human-Object Interactioncited by 78
  25. 2023HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Videocited by 78
  26. 2022Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networkscited by 74
  27. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 72
  28. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 70
  29. 2021Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networkscited by 69
  30. 2023CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objectscited by 64
  31. 2022Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Datasetcited by 59
  32. 2022NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimationcited by 58
  33. 2022Zero-Shot Category-Level Object Pose Estimationcited by 57
  34. 2022CLA-NeRF: Category-Level Articulated Neural Radiance Fieldcited by 56
  35. 2023CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesiscited by 56
  36. 2024InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Modelscited by 56
  37. 2022OPD: Single-view 3D Openable Part Detectioncited by 54
  38. 2022Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusionscited by 54

50 most cited of 495 All 495 (JSON)