# Single Shot 6D Object Pose Estimation

arXiv: https://arxiv.org/abs/2004.12729 · HTML: https://mlsanity.com/paper/2004.12729/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping](https://mlsanity.com/paper/2309.07970/) (2023)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking](https://mlsanity.com/paper/2204.07049/) (2022)
- [SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models](https://mlsanity.com/paper/2509.09090/) (2025)
- [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)
- [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)
- [SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios](https://mlsanity.com/paper/2403.09317/) (2024)
- [CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration](https://mlsanity.com/paper/2210.03173/) (2022)
- [ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose Estimation in Bin-Picking](https://mlsanity.com/paper/2502.01004/) (2025)
- [Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction](https://mlsanity.com/paper/2103.09696/) (2021)
- [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)
- [An Integrated Approach to Robotic Object Grasping and Manipulation](https://mlsanity.com/paper/2411.13205/) (2024)
- [Deterministic Object Pose Confidence Region Estimation](https://mlsanity.com/paper/2506.22720/) (2025)
- [DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects](https://mlsanity.com/paper/2510.10933/) (2025)
- [G6D: Geometric Learning-Free RGB-D 6D Pose Solver for Robotic Manipulation](https://mlsanity.com/paper/2609.23566/) (2026)

## 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.