# 6-DoF Object Pose from Semantic Keypoints

arXiv: https://arxiv.org/abs/1703.04670 · HTML: https://mlsanity.com/paper/1703.04670/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2017.

## Built on by (most cited first, 30 of 89)

- [Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization](https://mlsanity.com/paper/2107.04649/) (2021)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (2022)
- [OnePose: One-Shot Object Pose Estimation without CAD Models](https://mlsanity.com/paper/2205.12257/) (2022)
- [Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation](https://mlsanity.com/paper/2104.14639/) (2021)
- [FoundPose: Unseen Object Pose Estimation with Foundation Features](https://mlsanity.com/paper/2311.18809/) (2023)
- [DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision](https://mlsanity.com/paper/2105.06464/) (2021)
- [Zero-Shot Category-Level Object Pose Estimation](https://mlsanity.com/paper/2204.03635/) (2022)
- [SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2106.14193/) (2021)
- [Coupled Iterative Refinement for 6D Multi-Object Pose Estimation](https://mlsanity.com/paper/2204.12516/) (2022)
- [Wide-Depth-Range 6D Object Pose Estimation in Space](https://mlsanity.com/paper/2104.00337/) (2021)
- [OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation](https://mlsanity.com/paper/2203.01072/) (2022)
- [Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image](https://mlsanity.com/paper/2109.06161/) (2021)
- [NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation](https://mlsanity.com/paper/2101.12378/) (2021)
- [StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation](https://mlsanity.com/paper/2109.10115/) (2021)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [Single-view robot pose and joint angle estimation via render & compare](https://mlsanity.com/paper/2104.09359/) (2021)
- [Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation](https://mlsanity.com/paper/2303.12246/) (2023)
- [SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2310.17359/) (2023)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [VS-Net: Voting with Segmentation for Visual Localization](https://mlsanity.com/paper/2105.10886/) (2021)
- [Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting](https://mlsanity.com/paper/2104.02527/) (2021)
- [Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features](https://mlsanity.com/paper/2209.05624/) (2022)
- [Neural Correspondence Field for Object Pose Estimation](https://mlsanity.com/paper/2208.00113/) (2022)
- [Focal Length and Object Pose Estimation via Render and Compare](https://mlsanity.com/paper/2204.05145/) (2022)
- [SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation](https://mlsanity.com/paper/2208.02129/) (2022)
- [MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation](https://mlsanity.com/paper/2110.00464/) (2021)
- [Optimal Pose and Shape Estimation for Category-level 3D Object Perception](https://mlsanity.com/paper/2104.08383/) (2021)
- [Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose](https://mlsanity.com/paper/2110.14213/) (2021)
- [CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network](https://mlsanity.com/paper/2303.16874/) (2023)
- [PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation](https://mlsanity.com/paper/2102.12096/) (2021)

## Abstract (arXiv)

This paper presents a novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior work, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training image data. Furthermore, the approach can be applied to instance- and class-based pose recovery. Empirically, we show that the proposed approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios with a cluttered background. For class-based object pose estimation, state-of-the-art accuracy is shown on the large-scale PASCAL3D+ dataset.