mlsanityData to 14 Oct 2026

Spatial Feature Mapping for 6DoF Object Pose Estimation

arXiv 2206.01831 ↗First posted 3 Jun 2022cs.CVcited by 3

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Abstract (arXiv)

This work aims to estimate 6Dof (6D) object pose in background clutter. Considering the strong occlusion and background noise, we propose to utilize the spatial structure for better tackling this challenging task. Observing that the 3D mesh can be naturally abstracted by a graph, we build the graph using 3D points as vertices and mesh connections as edges. We construct the corresponding mapping from 2D image features to 3D points for filling the graph and fusion of the 2D and 3D features. Afterward, a Graph Convolutional Network (GCN) is applied to help the feature exchange among objects' points in 3D space. To address the problem of rotation symmetry ambiguity for objects, a spherical convolution is utilized and the spherical features are combined with the convolutional features that are mapped to the graph. Predefined 3D keypoints are voted and the 6DoF pose is obtained via the fitting optimization. Two scenarios of inference, one with the depth information and the other without it are discussed. Tested on the datasets of YCB-Video and LINEMOD, the experiments demonstrate the effectiveness of our proposed method.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 42 of its 86 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

42 of 86 references.

References · 42

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  5. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  6. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 651
  7. 2018Spherical CNNscited by 456
  8. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  9. 2015Recent Advances in Convolutional Neural Networkscited by 360
  10. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  11. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  12. 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 278
30 more
  1. 2017Learning SO(3) Equivariant Representations with Spherical CNNscited by 248
  2. 2021Vision-Language Transformer and Query Generation for Referring Segmentationcited by 240
  3. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  4. 2019Semantic Graph Convolutional Networks for 3D Human Pose Regressioncited by 204
  5. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  6. 20193D Hand Shape and Pose Estimation from a Single RGB Imagecited by 174
  7. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  8. 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
  9. 2020HOPE-Net: A Graph-based Model for Hand-Object Pose Estimationcited by 112
  10. 2019Boundary-Aware Feature Propagation for Scene Segmentationcited by 107
  11. 2018Segmentation-driven 6D Object Pose Estimationcited by 104
  12. 2019Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformationcited by 103
  13. 2021Recovering the Unbiased Scene Graphs from the Biased Onescited by 71
  14. 2018Explaining the Ambiguity of Object Detection and 6D Pose From Visual Datacited by 68
  15. 2019Semantic Correlation Promoted Shape-Variant Context for Segmentationcited by 61
  16. 2022Instance-Specific Feature Propagation for Referring Segmentationcited by 45
  17. 2022Flow-Guided Sparse Transformer for Video Deblurringcited by 43
  18. 2016Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimationcited by 34
  19. 2019Feature Boosting Network For 3D Pose Estimationcited by 26
  20. 2018Locating Objects Without Bounding Boxescited by 25
  21. 2021Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learningcited by 19
  22. 2021Few-Shot Segmentation with Global and Local Contrastive Learningcited by 17
  23. 2020Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentationcited by 15
  24. 2021Towards Enhancing Fine-grained Details for Image Mattingcited by 12
  25. 2021Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognitioncited by 12
  26. 2021M2IOSR: Maximal Mutual Information Open Set Recognitioncited by 12
  27. 2016Fast Single Shot Detection and Pose Estimationcited by 11
  28. 2018Fast and Accurate Intrinsic Symmetry Detectioncited by 7
  29. 2022Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitivescited by 4
  30. 2020Object 6D Pose Estimation with Non-local Attentioncited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 40
  2. 20236D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimationcited by 24
  3. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21