SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation-Reference-Cited by-同舟云学术

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SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Lin Haitao¹,Liu Zichang¹,Cheang Chilam¹,Fu Yanwei¹,Guo Guodong²,Xue Xiangyang¹

Affiliation:

1. Fudan University

2. IDL, Baidu Research

Funder

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879530.pdf?arnumber=9879530

Reference66 articles.

1. PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation

2. 6-DoF object pose from semantic keypoints

3. kpam: Keypoint affordances for category-level robotic manipulation;manuelli;ArXiv Preprint,2019

4. Object recognition from local scale-invariant features

5. Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis

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1. Dual-COPE: A novel prior-based category-level object pose estimation network with dual Sim2Real unsupervised domain adaptation module;Computers & Graphics;2024-11

2. Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping;Applied Sciences;2024-09-07

3. EdgePose: An Edge Attention Network for 6D Pose Estimation;Mathematics;2024-08-23

4. Category-Level Object Pose Estimation with Statistic Attention;Sensors;2024-08-19

5. LA-Net: An End-to-End Category-Level Object Attitude Estimation Network Based on Multi-Scale Feature Fusion and an Attention Mechanism;Electronics;2024-07-17

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