mlsanityData to 14 Oct 2026

DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation

arXiv 2204.09983 ↗First posted 21 Apr 2022cs.CVcited by 12

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Abstract (arXiv)

Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages ofestimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2)We leverage the uncertainty ofthe estimated depth map to improve accuracy and robustness ofthe output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 40 of its 50 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

40 of 50 references.

References · 40

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  12. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
28 more
  1. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  2. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  3. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  4. 2016DSAC - Differentiable RANSAC for Camera Localizationcited by 266
  5. 2017SSD-6D: Making RGB-based 3D detection and 6D pose estimation great againcited by 243
  6. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  7. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  8. 2020Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructionscited by 208
  9. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  10. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
  11. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  12. 2020EPOS: Estimating 6D Pose of Objects with Symmetriescited by 148
  13. 2021GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimatorcited by 148
  14. 2019Self-Supervised Monocular Depth Hintscited by 127
  15. 2020HybridPose: 6D Object Pose Estimation under Hybrid Representationscited by 107
  16. 2018Segmentation-driven 6D Object Pose Estimationcited by 104
  17. 2019Single-Stage 6D Object Pose Estimationcited by 88
  18. 2021SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimationcited by 88
  19. 2018Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimationcited by 74
  20. 2020Detail Preserved Point Cloud Completion via Separated Feature Aggregationcited by 70
  21. 2018Explaining the Ambiguity of Object Detection and 6D Pose From Visual Datacited by 68
  22. 2019PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrievalcited by 68
  23. 2019End-to-End Learnable Geometric Vision by Backpropagating PnP Optimizationcited by 65
  24. 2020MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusioncited by 46
  25. 2021Single Image Depth Prediction with Wavelet Decompositioncited by 34
  26. 2021Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioningcited by 27
  27. 2021PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimationcited by 14
  28. 2019Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objectscited by 10

Built on by

Cited by year

Citing papers per year: 2023: 7; 2024: 3; 2025: 2.

12 papers.

  1. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 40
  2. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  3. 2023Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimationcited by 16
  4. 2025Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integrationcited by 11
  5. 2023TransPose: 6D Object Pose Estimation with Geometry-Aware Transformercited by 7
  6. 2024Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Trackercited by 3
  7. 2023Open Challenges for Monocular Single-shot 6D Object Pose Estimationcited by 2
  8. 2023S2P3: Self-Supervised Polarimetric Pose Predictioncited by 2
  9. 2025DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulationcited by 2
  10. 2023KVN: Keypoints Voting Network with Differentiable RANSAC for Stereo Pose Estimationcited by 0
  11. 2023Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Datacited by 0
  12. 2024KGpose: Keypoint-Graph Driven End-to-End Multi-Object 6D Pose Estimation via Point-Wise Pose Votingcited by 0