mlsanityData to 13 Oct 2026

UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

arXiv 2111.12580 ↗First posted 24 Nov 2021cs.CVcited by 25

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Abstract (arXiv)

Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimation, called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels. We also introduce a bidirectional filtering method between the predicted normalized object coordinate space (NOCS) map and observed point cloud, to not only make our teacher network more robust to the target domain but also to provide more reliable pseudo labels for the student network training. Extensive experimental results demonstrate the effectiveness of our proposed method both quantitatively and qualitatively. Notably, without leveraging target-domain GT labels, our proposed method achieved comparable or sometimes superior performance to existing methods that depend on the GT labels.

Key facts. Cited by 25 papers in the map (data through 13 Oct 2026). Builds on 35 of its 42 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

35 of 42 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
  3. 2017Mask R-CNNcited by 7,266
  4. 2016Pyramid Scene Parsing Networkcited by 2,474
  5. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,205
  6. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 770
  7. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 492
  8. 2019Confidence Regularized Self-Trainingcited by 418
  9. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  10. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 354
  11. 2019Bidirectional Learning for Domain Adaptation of Semantic Segmentationcited by 308
  12. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
23 more
  1. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  2. 2019DRIT++: Diverse Image-to-Image Translation via Disentangled Representationscited by 244
  3. 2017SSD-6D: Making RGB-based 3D detection and 6D pose estimation great againcited by 243
  4. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
  5. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  6. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  7. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  8. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 159
  9. 2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
  10. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  11. 2017Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Graspingcited by 138
  12. 2019xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 129
  13. 2020Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimationcited by 123
  14. 2021DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistencycited by 103
  15. 2020Category Level Object Pose Estimation via Neural Analysis-by-Synthesiscited by 100
  16. 2019DADA: Depth-aware Domain Adaptation in Semantic Segmentationcited by 97
  17. 2018MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objectscited by 95
  18. 2016Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challengecited by 70
  19. 2021Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networkscited by 69
  20. 2021Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimationcited by 45
  21. 2021Category-Level Metric Scale Object Shape and Pose Estimationcited by 40
  22. 2017SegICP: Integrated Deep Semantic Segmentation and Pose Estimationcited by 29
  23. 2020Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognitioncited by 15

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 4; 2023: 9; 2024: 9; 2025: 2; 2026: 1.

25 papers.

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  1. 2022Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networkscited by 74
  2. 2022Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Datasetcited by 59
  3. 2023HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimationcited by 48
  4. 2025Any6D: Model-free 6D Pose Estimation of Novel Objectscited by 47
  5. 2024Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose Estimationcited by 42
  6. 2023IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformationcited by 40
  7. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 40
  8. 2023TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimationcited by 32
  9. 2024EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognitioncited by 23
  10. 2022Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wildcited by 22
  11. 2024GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinementcited by 8
  12. 2024Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimationcited by 7
13 more
  1. 2022CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregationcited by 6
  2. 2023SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objectscited by 6
  3. 2023MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detectioncited by 4
  4. 2023Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimationcited by 4
  5. 2025MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Modelcited by 4
  6. 2023PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registrationcited by 3
  7. 2024Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Trackercited by 3
  8. 2023RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluationcited by 2
  9. 2023Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Posecited by 2
  10. 2024Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detectioncited by 1
  11. 2024Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose Estimationcited by 1
  12. 2024EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognitioncited by 1
  13. 2026GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Pickingcited by 0