scholarly journals A Hybrid 3D Registration Method of Augmented Reality for Intelligent Manufacturing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 181867-181883
Author(s):  
Xian Yang ◽  
Jingfan Yang ◽  
Hanwu He ◽  
Heen Chen
Keyword(s):  
Augmented Reality ◽  
Intelligent Manufacturing ◽  
3D Registration ◽  
Registration Method

scholarly journals An Improved 3D Registration Method of Mobile Augmented Reality for Urban Built Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Wu ◽  
Weitao Che ◽  
Bihui Huang
Keyword(s):  
Augmented Reality ◽  
Built Environment ◽  
Real Time ◽  
Smart Phone ◽  
Experimental Result ◽  
3D Registration ◽  
Registration Method ◽  
Time Performance ◽  
Building Recognition ◽  
Mobile Ar

3D registration plays a pivotal role in augmented reality (AR) system. The existing methods are not suitable to be applied directly in the mobile AR system for the built environment, with the reasons of poor real-time performance and robustness. This paper proposes an improved 3D registration method of mobile AR for built environment, which is based on SURFREAK and KLT. This method increases the building efficiency of algorithm descriptors and maintains the robustness of the algorithms. To implement and evaluate the registration method, a smart phone-based mobile AR system for built environment is developed. The experimental result shows that the improved method is endowed with higher real-time performance and robustness, and the mobile AR 3D registration can realize a favorable performance and efficiency in the complex built environment. The mobile AR system could be used in building recognition and information augmentation for built environment and further to facilitate location-based games, urban heritage tourism, urban planning, and smart city.

Augmented Reality Recognition Registration Method Based on Text Features

2020 ◽  
Vol 57 (2) ◽  
pp. 021502
Author(s):  
李雪婷 Li Xueting ◽  
党建武 Dang Jianwu ◽  
王阳萍 Wang Yangping ◽  
高凡一 Gao Fanyi
Keyword(s):  
Augmented Reality ◽  
Registration Method ◽  
Text Features

Validation of a New Method for 2D Fusion Imaging Registration in a System Prepared Only for 3D

2020 ◽  
Vol 27 (3) ◽  
pp. 468-472
Author(s):  
Andreas Edsfeldt ◽  
Björn Sonesson ◽  
Helena Rosén ◽  
Marcelo H. Petri ◽  
Kiattisak Hongku ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Radiation Exposure ◽  
Imaging System ◽  
Endovascular Aneurysm Repair ◽  
Fusion Imaging ◽  
Operating Table ◽  
3D Registration ◽  
Registration Method ◽  
Dose Area Product ◽  
Single Frame

Purpose: To validate a new 2D-3D registration method of fusion imaging during aortic repair in a system prepared only for 3D-3D registration and to compare radiation doses and accuracy. Materials and Methods: The study involved 189 patients, including 94 patients (median age 70 years; 85 men) who underwent abdominal endovascular aneurysm repair (EVAR) with 2D-3D fusion on an Artis zee imaging system and 95 EVAR patients (median age 70 years; 81 men) from a prior study who had 3D-3D registration done using cone beam computed tomography (CBCT). For the 2D-3D registration, an offline CBCT of the empty operating table was imported into the intraoperative dataset and superimposed on the preoperative computed tomography angiogram (CTA). Then 2 intraoperative single-frame 2D images of the skeleton were aligned with the patient’s skeleton on the preoperative CTA to complete the registration process. A digital subtraction angiogram was done to correct any misalignment of the aortic CTA volume. Values are given as the median [interquartile range (IQR) Q1, Q3]. Results: The 2D-3D registration had an accuracy of 4.0 mm (IQR 3.0, 5.0) after bone matching compared with the final correction with DSA (78% within 5 mm). By applying the 2D-3D protocol the radiation exposure (dose area product) from the registration of the fusion image was significantly reduced compared with the 3D-3D registration [1.12 Gy∙cm2 (IQR 0.41, 2.14) vs 43.4 Gy∙cm2 (IQR 37.1, 49.0), respectively; p<0.001). Conclusion: The new 2D-3D registration protocol based on 2 single-frame images avoids an intraoperative CBCT and can be used for fusion imaging registration in a system originally designed for 3D-3D only. This 2D-3D registration protocol is accurate and leads to a significant reduction in radiation exposure.

Improving Registration of Augmented Reality by Incorporating DCNNS into Visual SLAM

2018 ◽  
Vol 32 (12) ◽  
pp. 1855022 ◽  
Author(s):  
Yongbin Chen ◽  
Hanwu He ◽  
Heen Chen ◽  
Teng Zhu
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Recognition Algorithm ◽  
Scene Recognition ◽  
3D Registration ◽  
Deep Convolutional Neural Networks ◽  
Severe Problem ◽  
Localization And Mapping ◽  
Semantic Scene

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

Research on 3D Visualization of Underground Antique Tomb Based on Augmented Reality

2013 ◽  
Vol 336-338 ◽  
pp. 1434-1438 ◽  
Author(s):  
Yong Chang ◽  
Wen Peng Xu ◽  
Lei Wang
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
3D Model ◽  
3D Visualization ◽  
Outdoor Environment ◽  
3D Registration ◽  
Camera Position ◽  
Position And Orientation ◽  
To Receive ◽  
Electronic Compass

This paper researches on 3D visualization of underground antique tomb based on Augmented Reality .At first,this paper established 3D model of antique tomb with OpenGL in VC++6.0. In order to establish augmented reality 3D model of antique tomb, the OpenGL model of antique tomb was transferred into ARToolKit and realized 3D augmented reality visualization in ARToolKit. As ARToolKit uses computer vision techniques to calculate the real camera position and orientation relative to marked cards,so it is difficult to provide AR applications in outdoor environment. At last, this paper puts forth a new method to allow ARtoolkit to receive the data of GPS and 3D electronic compass, so it can make 3D registration with both computer vision and sensors.

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