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
收藏
页码：106 / 108
页数：3
相关论文
共 50 条
  • [21] Keypoint-graph-driven learning framework for object pose estimation
    Zhang, Shaobo
    Zhao, Wanqing
    Guan, Ziyu
    Peng, Xianlin
    Peng, Jinye
    2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 1065 - 1073
  • [22] Object pose estimation using a set of local radiographs of a part and its CAD model
    Koenig, A
    Gliere, A
    Rizo, P
    Bell, B
    Paul, B
    Anderson, J
    REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 16A AND 16B, 1997, 16 : 829 - 836
  • [23] Generating Synthetic Humans for Learning 3D Pose Estimation
    Aso, Kohei
    Hwang, Dong-Hyun
    Koike, Hideki
    2019 26TH IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES (VR), 2019, : 1519 - 1520
  • [24] Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem
    Charco, Jorge L.
    Sappa, Angel D.
    Vintimilla, Boris X.
    Velesaca, Henry O.
    VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, 2020, : 498 - 505
  • [25] Crane pose estimation using deep learning models and synthetic images
    Park G.
    Hong H.
    Jeong H.
    Kang H.
    Won M.
    Journal of Institute of Control, Robotics and Systems, 2021, 27 (04) : 312 - 319
  • [26] Object Detection and Pose Estimation based on Convolutional Neural Networks Trained with Synthetic Data
    Josifovski, Josip
    Kerzel, Matthias
    Pregizer, Christoph
    Posniak, Lukas
    Wermter, Stefan
    2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2018, : 6269 - 6276
  • [27] Image Translation Based Synthetic Data Generation for Industrial Object Detection and Pose Estimation
    Yang, Xu
    Fan, Xiumin
    Wang, Jinge
    Lee, Kunbo
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (03) : 7201 - 7208
  • [28] Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation
    Tremblay, Jonathan
    To, Thang
    Birchfield, Stan
    PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2018, : 2119 - 2122
  • [29] Synthetic Depth Transfer for Monocular 3D Object Pose Estimation in the Wild
    Kao, Yueying
    Li, Weiming
    Wang, Qiang
    Lin, Zhouchen
    Kim, Wooshik
    Hong, Sunghoon
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 11221 - 11228
  • [30] SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
    Pan, Haoran
    Zhou, Jun
    Liu, Yuanpeng
    Lu, Xuequan
    Wang, Weiming
    Yan, Xuefeng
    Wei, Mingqiang
    COMPUTER GRAPHICS FORUM, 2022, 41 (07) : 371 - 381
12345