Synthetic Learning Set for Object Pose Estimation: Initial Experiments

被引：0

作者：

Lee, Joo-Haeng ^{[1
,2
]}

Yun, Woo-Han ^{[1
]}

Lee, Jaeyeon ^{[1
]}

Kim, Jaehong ^{[1
]}

机构：

[1] ETRI, Human Machine Interact Grp, Daejeon 34129, South Korea

[2] Univ Sci & Technol, Comp Software Dept, Daejeon 34113, South Korea

来源：

2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI) | 2017年

关键词：

Synthetic learning set; pose estimation; machine learning; robot manipulation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We summarize a method to generate a synthetic learning set for object pose estimation in robotic manipulation tasks. Exploiting modern computer graphics techniques, our synthetic learning set satisfies the requirements both in quantitative diversity and qualitative precision. We report the partial results of initial experiments and discuss some future research directions.

引用

页码：106 / 108

页数：3

共 50 条

[1] Methods of RVD object pose estimation and experiments
Shang, Yang
He, Yan
Wang, Weihua
Yu, Qifeng
SECOND INTERNATIONAL CONFERENCE ON SPACE INFORMATION TECHNOLOGY, PTS 1-3, 2007, 6795
[2] Learning Orientation Distributions for Object Pose Estimation
Okorn, Brian
Xu, Mengyun
Hebert, Martial
Held, David
2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 10580 - 10587
[3] Object Recognition and Pose Estimation base on Deep Learning
Xue, Li-wei
Chen, Li-guo
Liu, Ji-zhu
Wang, Yang-jun
Shen, Qi
Huang, Hai-bo
2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017), 2017, : 1288 - 1293
[4] Online Learning of Visibility and Appearance for Object Pose Estimation
Lee, Bhoram
Lee, Daniel D.
2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), 2016, : 2792 - 2798
[5] Deep Learning-Based 6-DoF Object Pose Estimation Considering Synthetic Dataset
Zheng, Tianyu
Zhang, Chunyan
Zhang, Shengwen
Wang, Yanyan
SENSORS, 2023, 23 (24)
[6] Underwater Object Detection and Pose Estimation using Deep Learning
Jeon, MyungHwan
Lee, Yeongjun
Shin, Young-Sik
Jang, Hyesu
Kim, Ayoung
IFAC PAPERSONLINE, 2019, 52 (21): : 78 - 81
[7] Planar Pose Estimation Using Object Detection and Reinforcement Learning
Rasmussen, Frederik Norby
Andersen, Sebastian Terp
Grossmann, Bjarne
Boukas, Evangelos
Nalpantidis, Lazaros
COMPUTER VISION SYSTEMS (ICVS 2019), 2019, 11754 : 353 - 365
[8] Pose Guided RGBD Feature Learning for 3D Object Pose Estimation
Balntas, Vassileios
Doumanoglou, Andreas
Sahin, Caner
Sock, Juil
Kouskouridas, Rigas
Kim, Tae-Kyun
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, : 3876 - 3884
[9] Transfer Learning for Driver Pose Estimation from Synthetic Data
Sagmeister, Daniel
Schoerkhuber, Dominik
Nezveda, Matej
Stiedl, Fabian
Schimkowitsch, Maria
Gelautz, Margrit
2023 IEEE INTELLIGENT VEHICLES SYMPOSIUM, IV, 2023,
[10] Object recognition and pose estimation for modular manipulation system: overview and initial results
Yun, Woo-han
Lee, Jaeyeon
Lee, Joo-Haeng
Kim, Jaehong
2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI), 2017, : 198 - 201

← 1 2 3 4 5 →