mlsanityData to 14 Oct 2026

PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments

arXiv 2312.15130 ↗First posted 23 Dec 2023cs.CVcited by 11

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Abstract (arXiv)

We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios. PACE provides a large-scale real-world benchmark for both instance-level and category-level settings. The benchmark consists of 55K frames with 258K annotations across 300 videos, covering 238 objects from 43 categories and featuring a mix of rigid and articulated items in cluttered scenes. To annotate the real-world data efficiently, we develop an innovative annotation system with a calibrated 3-camera setup. Additionally, we offer PACE-Sim, which contains 100K photo-realistic simulated frames with 2.4M annotations across 931 objects. We test state-of-the-art algorithms in PACE along two tracks: pose estimation, and object pose tracking, revealing the benchmark's challenges and research opportunities. Our benchmark code and data is available on https://github.com/qq456cvb/PACE.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

38 of 49 references.

References · 38

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26 more
  1. 2019Category-Level Articulated Object Pose Estimationcited by 159
  2. 2020Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotationscited by 155
  3. 2021GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimatorcited by 148
  4. 2018Scan2CAD: Learning CAD Model Alignment in RGB-D Scanscited by 142
  5. 2021DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistencycited by 103
  6. 2021SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddingscited by 95
  7. 2019PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Trackingcited by 91
  8. 2020se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domainscited by 90
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  10. 2021CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Cloudscited by 84
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  12. 20196-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypointscited by 78
  13. 2022Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Datasetcited by 59
  14. 2021SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimationcited by 53
  15. 2022HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarioscited by 53
  16. 2023NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotationscited by 53
  17. 2023HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimationcited by 49
  18. 2023GenPose: Generative Category-level Object Pose Estimation via Diffusion Modelscited by 48
  19. 2023HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructionscited by 48
  20. 2021StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimationcited by 40
  21. 2022Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objectscited by 30
  22. 2018A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Trackingcited by 21
  23. 2022Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimationcited by 19
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  25. 2023Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Trackingcited by 7
  26. 2022CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregationcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 7.

11 papers.