mlsanityData to 14 Oct 2026

Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking

arXiv 1912.12125 ↗First posted 6 Dec 2019cs.CV cs.AI cs.ROcited by 25

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Abstract (arXiv)

In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating real-world scenes is proposed. To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning-based approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http://www.bin-picking.ai/en/dataset.html.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Dec 2019.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 5; 2024: 2; 2025: 4; 2026: 2.

25 papers.

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  1. 2021SuctionNet-1Billion: A Large-Scale Benchmark for Suction Graspingcited by 32
  2. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  3. 2022Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Pickingcited by 20
  4. 20246DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splattingcited by 11
  5. 2022Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detectioncited by 8
  6. 2022Dataset of Industrial Metal Objectscited by 8
  7. 2022Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimationcited by 7
  8. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  9. 2021IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objectscited by 6
  10. 2022Towards Robust Part-aware Instance Segmentation for Industrial Bin Pickingcited by 5
  11. 2023Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimationcited by 4
  12. 2021Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimationcited by 3
13 more
  1. 2023Learning Better Keypoints for Multi-Object 6DoF Pose Estimationcited by 3
  2. 2024A Low-Cost, High-Speed, and Robust Bin Picking System for Factory Automation Enabled by a Non-Stop, Multi-View, and Active Vision Schemecited by 3
  3. 2022CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaborationcited by 2
  4. 2023Open Challenges for Monocular Single-shot 6D Object Pose Estimationcited by 2
  5. 2025Quaternion Approximation Networks for Enhanced Image Classification and Oriented Object Detectioncited by 1
  6. 2025Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Cloudscited by 1
  7. 2021Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validationcited by 0
  8. 2021Precise Object Placement with Pose Distance Estimations for Different Objects and Gripperscited by 0
  9. 2023NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarioscited by 0
  10. 2025Automating 3D Dataset Generation with Neural Radiance Fieldscited by 0
  11. 2025Geo6DPose: Fast Zero-Shot 6D Object Pose Estimation via Geometry-Filtered Feature Matchingcited by 0
  12. 2026Towards a Modular Bin-picking Framework for Handling Object Pose Uncertaintiescited by 0
  13. 2026Prior-free relative 6D pose estimation of multiple object instancescited by 0