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add paper and code
Guptajakala Mar 6, 2022
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Guptajakala Jun 22, 2023
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15 changes: 11 additions & 4 deletions README.md
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Expand Up @@ -1474,7 +1474,7 @@ This kind of methods can be regarded as regression-based methods.

**[IROS]** Robust Ego and Object 6-DoF Motion Estimation and Tracking, [[paper](https://arxiv.org/pdf/2007.13993.pdf)]

**[IROS]** se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains, [[paper](https://arxiv.org/pdf/2007.13866.pdf)]
**[IROS]** se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains, [[paper](https://arxiv.org/pdf/2007.13866.pdf)] [[code](https://github.com/wenbowen123/iros20-6d-pose-tracking)]

**[arXiv]** Learning Orientation Distributions for Object Pose Estimation, [[paper](https://arxiv.org/pdf/2007.01418.pdf)]

Expand Down Expand Up @@ -1888,8 +1888,14 @@ The partial-view point cloud will be aligned to the complete shape in order to o

#### 2.3.1 Category-level 6D pose estimation

***2023:***

**[CVPR]** BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects, [[paper](https://arxiv.org/abs/2303.14158)] [[code](https://github.com/NVlabs/BundleSDF)]

***2021:***

**[IROS]** BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models, [[paper](https://arxiv.org/abs/2108.00516)] [[code](https://github.com/wenbowen123/BundleTrack)]

**[arXiv]** Towards Real-World Category-level Articulation Pose Estimation, [[paper](https://arxiv.org/pdf/2105.03260.pdf)]

**[arXiv]** CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds, [[paper](https://arxiv.org/pdf/2104.03437.pdf)]
Expand Down Expand Up @@ -2911,10 +2917,11 @@ In this situation, there exist no 3D models, an the 6-DoF grasps are estimated f

### 5.2 Grasp Affordance

***2021:***
***2022:***

**[ICRA]** CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation, [[paper](https://arxiv.org/pdf/2109.09163.pdf)] [[code](https://sites.google.com/view/catgrasp)]

**[arXiv]** CaTGrasp: Learning Category-Level Task-Relevant
Grasping in Clutter from Simulation, [[paper](https://arxiv.org/pdf/2109.09163.pdf)] [[code](https://sites.google.com/view/catgrasp)]
***2021:***

**[CVPR]** 3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding, [[paper](https://arxiv.org/pdf/2103.16397.pdf)]

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