Realtime RGB-Based 3D Object Pose Detection Using Convolutional Neural Networks

被引：7

作者：

Liu, Jin ^{[1
]}

He, Sheng ^{[1
]}

Tao, Yiting ^{[1
]}

Liu, Daifei ^{[2
]}

机构：

[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China

[2] Changsha Univ Sci &Technol, Sch Energy & Power Engn, Changsha 410004, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2020年 / 20卷 / 20期

基金：

中国国家自然科学基金;

关键词：

Quaternions; Three-dimensional displays; Cameras; Training; Object detection; Two dimensional displays; Sensors; 3D pose detection; convolutional neural network;

D O I：

10.1109/JSEN.2019.2946279

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We present a new approach to efficiently detect the 3D pose of objects in images. The approach uses a single neural network we call TQ-Net to predict the translation vector and the quaternion, after which the quaternion is converted to a rotation matrix, and through a projection algorithm, we can project eight points surrounding the object to the image and connect them to form a 3D bounding box. Considering there is a constraint existing in the quaternion, we add a normalization layer to get a more precise result. Experiments show that our approach performs well and can detect 3D poses in real time. Our approach processes images with a resolution of 1280*720 at 61 frames per second on a GTX 1080 GPU.

引用

页码：11812 / 11819

页数：8

共 50 条

[21] SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Kehl, Wadim
Manhardt, Fabian
Tombari, Federico
Ilic, Slobodan
Navab, Nassir
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2017, : 1530 - 1538
[22] Depth-aware Convolutional Neural Networks for accurate 3D Pose Estimation in RGB-D Images
Porzi, Lorenzo
Penate-Sanchez, Adrian
Ricci, Elisa
Moreno-Noguer, Francesc
[J]. 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, : 5777 - 5783
[23] 3D Object Classification using 3D Racah Moments Convolutional Neural Networks
Mesbah, Abderrahim
Berrahou, Aissam
El Alami, Abdelmajid
Berrahou, Nadia
Berbia, Hassan
Qjidaa, Hassan
[J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON NETWORKING, INFORMATION SYSTEMS & SECURITY (NISS19), 2019,
[24] 3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
Park, Sungheon
Hwang, Jihye
Kwak, Nojun
[J]. COMPUTER VISION - ECCV 2016 WORKSHOPS, PT III, 2016, 9915 : 156 - 169
[25] Deep 3D Pose Dictionary: 3D Human Pose Estimation from Single RGB Image Using Deep Convolutional Neural Network
Elbasiony, Reda
Gomaa, Walid
Ogata, Tetsuya
[J]. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III, 2018, 11141 : 310 - 320
[26] Two-Stage RGB-Based Action Detection Using Augmented 3D Poses
Papadopoulos, Konstantinos
Ghorbel, Enjie
Baptista, Renato
Aouada, Djamila
Ottersten, Bjoern
[J]. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT I, 2019, 11678 : 26 - 35
[27] Fast 3D Object Detection with RGB-D Images Using Graph Convolutional Network
Takahashi, Masahiro
Kitsukawa, Takumi
Moro, Alessandro
Umeda, Kazunori
[J]. 2022 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII 2022), 2022, : 395 - 400
[28] Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural networks and algebraic geometry
Hasan, Md Kamrul
Calvet, Lilian
Rabbani, Navid
Bartoli, Adrien
[J]. MEDICAL IMAGE ANALYSIS, 2021, 70
[29] Point Cloud Object Recognition using 3D Convolutional Neural Networks
Soares, Marcelo Borghetti
Wermter, Stefan
[J]. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
[30] Lung Cancer Detection using 3D Convolutional Neural Networks
Pradhan, Adarsh
Sarma, Bhaskarjyothi
Dey, Bhiman Kr
[J]. 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2020), 2020, : 765 - 770

← 1 2 3 4 5 →