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

arXiv: https://arxiv.org/abs/1912.12125 · HTML: https://mlsanity.com/paper/1912.12125/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking](https://mlsanity.com/paper/2204.07049/) (2022)
- [6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting](https://mlsanity.com/paper/2412.01543/) (2024)
- [Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection](https://mlsanity.com/paper/2203.05187/) (2022)
- [Dataset of Industrial Metal Objects](https://mlsanity.com/paper/2208.04052/) (2022)
- [Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation](https://mlsanity.com/paper/2210.08123/) (2022)
- [Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes](https://mlsanity.com/paper/2101.04781/) (2021)
- [IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objects](https://mlsanity.com/paper/2111.08614/) (2021)
- [Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking](https://mlsanity.com/paper/2203.02767/) (2022)
- [Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimation](https://mlsanity.com/paper/2311.09500/) (2023)
- [Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2104.07528/) (2021)
- [Learning Better Keypoints for Multi-Object 6DoF Pose Estimation](https://mlsanity.com/paper/2308.07827/) (2023)
- [A Low-Cost, High-Speed, and Robust Bin Picking System for Factory Automation Enabled by a Non-Stop, Multi-View, and Active Vision Scheme](https://mlsanity.com/paper/2410.00706/) (2024)
- [CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration](https://mlsanity.com/paper/2210.03173/) (2022)
- [Open Challenges for Monocular Single-shot 6D Object Pose Estimation](https://mlsanity.com/paper/2302.11827/) (2023)
- [Quaternion Approximation Networks for Enhanced Image Classification and Oriented Object Detection](https://mlsanity.com/paper/2509.05512/) (2025)
- [Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds](https://mlsanity.com/paper/2512.07211/) (2025)
- [Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation](https://mlsanity.com/paper/2106.06684/) (2021)
- [Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers](https://mlsanity.com/paper/2110.00992/) (2021)
- [NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios](https://mlsanity.com/paper/2311.09269/) (2023)
- [Automating 3D Dataset Generation with Neural Radiance Fields](https://mlsanity.com/paper/2503.15997/) (2025)
- [Geo6DPose: Fast Zero-Shot 6D Object Pose Estimation via Geometry-Filtered Feature Matching](https://mlsanity.com/paper/2512.10674/) (2025)
- [Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties](https://mlsanity.com/paper/2607.13698/) (2026)
- [Prior-free relative 6D pose estimation of multiple object instances](https://mlsanity.com/paper/2609.08949/) (2026)

## 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.