mlsanityData to 14 Oct 2026

Unseen Object 6D Pose Estimation: A Benchmark and Baselines

arXiv 2206.11808 ↗First posted 23 Jun 2022cs.CV cs.ROcited by 12

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Abstract (arXiv)

Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet.net/unseen6d

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

31 of 60 references.

References · 31

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  8. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  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. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 336
  12. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 321
19 more
  1. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  2. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  3. 2017SSD-6D: Making RGB-based 3D detection and 6D pose estimation great againcited by 243
  4. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  5. 2017Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detectioncited by 209
  6. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  7. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 181
  8. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
  9. 2020EPOS: Estimating 6D Pose of Objects with Symmetriescited by 148
  10. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
  11. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  12. 2018Scan2CAD: Learning CAD Model Alignment in RGB-D Scanscited by 142
  13. 2019LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimationcited by 89
  14. 2018Explaining the Ambiguity of Object Detection and 6D Pose From Visual Datacited by 68
  15. 2021Deep Hough Voting for Robust Global Registrationcited by 53
  16. 2019Multi-path Learning for Object Pose Estimation Across Domainscited by 52
  17. 2021OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulationcited by 24
  18. 2020Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapescited by 11
  19. 2018Estimating 6D Pose From Localizing Designated Surface Keypointscited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 1; 2025: 3; 2026: 2.

12 papers.

  1. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 40
  2. 2023A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospectscited by 30
  3. 2023GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusioncited by 22
  4. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  5. 2023One-Shot Imitation Learning: A Pose Estimation Perspectivecited by 21
  6. 2025Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integrationcited by 11
  7. 2023MFOS: Model-Free & One-Shot Object Pose Estimationcited by 10
  8. 2023KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matchingcited by 7
  9. 2025Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approachcited by 1
  10. 2025SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Referencecited by 1
  11. 2026HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labelingcited by 0
  12. 2026Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applicationscited by 0