# G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

arXiv: https://arxiv.org/abs/2003.11089 · HTML: https://mlsanity.com/paper/2003.11089/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 30 of 35)

- [FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation](https://mlsanity.com/paper/2103.02242/) (2021)
- [FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism](https://mlsanity.com/paper/2103.07054/) (2021)
- [GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting](https://mlsanity.com/paper/2203.07918/) (2022)
- [Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset](https://mlsanity.com/paper/2206.15436/) (2022)
- [SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation](https://mlsanity.com/paper/2311.11125/) (2023)
- [OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation](https://mlsanity.com/paper/2203.01072/) (2022)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement](https://mlsanity.com/paper/2207.08082/) (2022)
- [Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation](https://mlsanity.com/paper/2203.14531/) (2022)
- [StablePose: Learning 6D Object Poses from Geometrically Stable Patches](https://mlsanity.com/paper/2102.09334/) (2021)
- [PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation](https://mlsanity.com/paper/2108.09916/) (2021)
- [Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space](https://mlsanity.com/paper/2111.07383/) (2021)
- [HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation](https://mlsanity.com/paper/2311.12588/) (2023)
- [Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2308.05438/) (2023)
- [DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation](https://mlsanity.com/paper/2210.05232/) (2022)
- [S2Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning](https://mlsanity.com/paper/2208.00874/) (2022)
- [GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement](https://mlsanity.com/paper/2404.11139/) (2024)
- [TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer](https://mlsanity.com/paper/2310.16279/) (2023)
- [RDPN6D: Residual-based Dense Point-wise Network for 6Dof Object Pose Estimation Based on RGB-D Images](https://mlsanity.com/paper/2405.08483/) (2024)
- [SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation](https://mlsanity.com/paper/2208.08338/) (2022)
- [NeRF-Feat: 6D Object Pose Estimation using Feature Rendering](https://mlsanity.com/paper/2406.13796/) (2024)
- [BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation](https://mlsanity.com/paper/2205.03536/) (2022)
- [Learning Better Keypoints for Multi-Object 6DoF Pose Estimation](https://mlsanity.com/paper/2308.07827/) (2023)
- [6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping](https://mlsanity.com/paper/2208.14288/) (2022)
- [Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation](https://mlsanity.com/paper/2212.04632/) (2022)
- [Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction](https://mlsanity.com/paper/2308.08518/) (2023)
- [FastPoseCNN: Real-Time Monocular Category-Level Pose and Size Estimation Framework](https://mlsanity.com/paper/2406.11063/) (2024)
- [SABER-6D: Shape Representation Based Implicit Object Pose Estimation](https://mlsanity.com/paper/2408.05867/) (2024)
- [A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation](https://mlsanity.com/paper/2501.01993/) (2024)

## Abstract (arXiv)

In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. Specifically, our network consists of three steps. First, we extract the coarse object point cloud from the RGB-D image by 2D detection. Second, we feed the coarse object point cloud to a translation localization network to perform 3D segmentation and object translation prediction. Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation. In the third step, we define point-wise embedding vector features to capture viewpoint-aware information. To calculate more accurate rotation, we adopt a rotation residual estimator to estimate the residual between initial rotation and ground truth, which can boost initial pose estimation performance. Our proposed G2L-Net is real-time despite the fact multiple steps are stacked via the proposed coarse-to-fine framework. Extensive experiments on two benchmark datasets show that G2L-Net achieves state-of-the-art performance in terms of both accuracy and speed.