6D位姿估计论⽂及代码汇总，持续更新~~~
6D位姿估计 论⽂及代码汇总，持续更新中~~
⽂章⽬录
综述
1. Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview —
Arxiv
1. PAM:Point-wise Attention Module for 6D Object Pose Estimation —
2. PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation —
3. — —
4. Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting —
5. Single-stage Keypoint-based Category-level Object Pose Estimation from an RGB Image — —
6. A Dynamic Keypoints Selection Network for 6DoF Pose Estimation —
7. T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
8. ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose
Estimation —
9. Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images —
—
10. PyraPose: Feature Pyramids for Fast and Accurate Object Pose Estimation under Domain Shift — —
11. 6D Object Pose Estimation using Keypoints and Part Affinity Fields —
12. Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation —
13. 6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning —
14. Spatial Attention Improves Iterative 6D Object Pose Estimation —
IEEE 2021
1. Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
NIPS 2021
1. Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
2. Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of
Object Poses in 3D Space — —
ICCV 2021
1. StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation —
2. — —
3. RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering — —
ICCV 2021 work shop
1. Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization —
CVPR 2021
1. — —
2. DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency —
3. StablePose: Learning 6D Object Poses from Geometrically Stable Patches —
4. FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation —
AAAI 2021
1. SD-Pose: Semantic Decomposition for Cross-Domain 6D Object Pose Estimation —
WACV 2021
1. —
IROS 2021
1. KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation —
ACCV 2020
1. A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking — Best Paper —
ECCV 2020
1. CosyPose：Consistent multi-view multi-object 6D pose estimation — — —
2. Self6D：Self-Supervised Monocular 6D Object Pose Estimation — Oral —
3. NOL：Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images — Spotlight — —
4. — —
5. Shape Prior Deformation：Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation — —
6. Few-Shot Viewpoint：Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild — —
7. Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild —CVPR 2020
1. PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation — — —
2. Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation —
3. Category-Level Articulated Object Pose Estimation — Oral —
4. MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion — —
5. EPOS: Estimating 6D Pose of Objects with Symmetries — —
6. G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features — —
7. — —
8. Multi-Path：Multi-path Learning for Object Pose Estimation Across Domains — —
9. — — —
10. Single-Stage 6D Object Pose Estimation — —
11. BPnP：End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization — —
active alignment
12. KeyPose: Multi-view 3D Labeling and Keypoint Estimation for Transparent Objects — ---- —
13. —
14. Reconstruct Locally, Localize Globally: A Model Free Method for Object Pose Estimation —
15. Learning deep network for detecting 3D object keypoints and 6D poses —
16. SSV：Self-Supervised Viewpoint Learning From Image Collections ---- —
17. Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation —
ICRA 2020
1. 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints — — —
2. RotationAnchor：Robust 6D Object Pose Estimation by Learning RGB-D Features — —
3. Self-Supervised 6D：Self-supervised 6D Object Pose Estimation for Robot Manipulation —
4. DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimatio —IROS 2020
1. — —
2. Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning —WACV 2020
1. PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation —
2. SymGAN: Orientation Estimation without Annotation for Symmetric Objects —
CVPR 2019
1. DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion — — —
2. — — —
3. PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation — Oral — —
4. Segmentation-driven 6D object pose estimation – —
ICCV 2019
1. — Oral —
2. Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation — —
3. DPOD: 6D pose object detector and refiner — —
4. Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data —
NuerIPS 2019
Neural Information Processing Systems (NuerIPS)
1. Learning to predict 3D objects with an interpolation-based differentiable renderer
ICRA 2019
1. MTTM：Multi-Task Template Matching for Object Detection, Segmentation and Pose Estimation Using Depth Images ISRR 2019
1. kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation —
RSS 2019
Robotics: Science and Systems (RSS)
1. PoseRBPF: A rao-blackwellized particle filter for 6D object pose tracking — —
CoRL 2019
1. Scene-level pose estimation for multiple instances of densely packed objects
CVPR 2018
1. YOLO-6D：Real-Time Seamless Single Shot 6D Object Pose Prediction — —
2. 3D-RCNN:Instance-level 3D object reconstruction via render-andcompare
3. Multiview consistency as supervisory signal for learning shape and pose prediction
ACCV 2018
1. iPose：Instance-Aware 6D Pose Estimation of Partly Occluded Objects —
ECCV 2018
1. Category-level 6D Object Pose Recovery in Depth Images —
2. A unified framework for multi-view multi-class object pose estimation —
3. — —
4. Implicit 3D orientation learning for 6D object detection from RGB images — —
5. End-to-end 6-DOF object pose estimation through differentiable rasterization
6. Making deep heatmaps robust to partial occlusions for 3D object pose estimation —
RSS 2018
1. — —
CoRL 2018
Conference on Robot Learning
1. Deep object pose estimation for semantic robotic grasping of household objects
2.
NeurIPS 2018
1. Keypointnet：Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning — Oral —
ICCV 2017
1. SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again — —
2. Real-Time Monocular Pose Estimation of 3D Objects using Temporally Consistent Local Color Histograms —
3. BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects
without Using Depth —
CVPR 2017
1. 3D bounding box estimation using deep learning and geometry
2. Global hypothesis generation for 6D object pose estimation
3. SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again — —
ICRA 2017
1. 6-DOF object pose from semantic keypoints — —
2. Multiview self-supervised deep learning for 6D pose estimation in the amazon picking challenge
CVPR 2016
1. Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image —
ECCV 2016
1. Deep learning of local RGB-D patches for 3D object detection and 6d pose estimation —
ICRA 2016
1. Depth-based object tracking using a robust gaussian filter
ICCV 2015
1. Learning analysis-by-synthesis for 6D pose estimation in RGB-D images
2. Posenet: A convolutional network for real-time 6-dof camera relocalization —
ECCV 2014
1. Learning 6D object pose estimation using 3D object coordinates
ACCV 2012
1. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes

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