mlsanityData to 7 Oct 2026

Single Shot 6D Object Pose Estimation

arXiv 2004.12729 ↗First posted 27 Apr 2020cs.CV cs.RO eess.IVcited by 19

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Abstract (arXiv)

In this paper, we introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. For this purpose, a fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task that is solved locally on the resulting volume elements. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously. Our approach does not require manually 6D pose-annotated real-world datasets and transfers to the real world, although being entirely trained on synthetic data. The proposed method is evaluated on public benchmark datasets, where we can demonstrate that state-of-the-art methods are significantly outperformed.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2020.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 3; 2024: 3; 2025: 4; 2026: 1.

19 papers.

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  1. 2023Language Embedded Radiance Fields for Zero-Shot Task-Oriented Graspingcited by 91
  2. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 39
  3. 2022Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Pickingcited by 20
  4. 2025SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Modelscited by 15
  5. 2022Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimationcited by 7
  6. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  7. 2023Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimationcited by 4
  8. 2021Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimationcited by 3
  9. 2024SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarioscited by 3
  10. 2022CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaborationcited by 2
  11. 2025ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose Estimation in Bin-Pickingcited by 2
  12. 2021Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interactioncited by 0
7 more
  1. 2021Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validationcited by 0
  2. 2021Precise Object Placement with Pose Distance Estimations for Different Objects and Gripperscited by 0
  3. 2023NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarioscited by 0
  4. 2024An Integrated Approach to Robotic Object Grasping and Manipulationcited by 0
  5. 2025Deterministic Object Pose Confidence Region Estimationcited by 0
  6. 2025DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objectscited by 0
  7. 2026G6D: Geometric Learning-Free RGB-D 6D Pose Solver for Robotic Manipulationcited by 0