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Correction: Efficacy of a virtual 3D Simulation-Based Digital Training Module for Building Dental technology Students' Long-Term Competency in Removable Partial Denture Design: Prospective Cohort Study

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JMIR serious games 2024年 12卷 e63113页

作者： KeXin Liu YaQian Xu ChaoYi Ma Na Yu FaBing Tan Yi Li YaXin Bai XiaoMing Fu JiaWu Wan DongQi Fan HuBin Yin MeiXi Chen HongJi Chen Lin Jiang JinLin Song Ping Ji XiaoHan Zhao MengWei Pang College of Stomatology Chongqing Medical University Chongqing China. Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences Chongqing China. Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education Chongqing China. Beijing Unidraw Virtual Reality Technology Research Institute Co Ltd Beijing China. State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing China.

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Lower Limb Balance Rehabilitation of Post-stroke Patients Using an Evaluating and Training Combined Augmented reality System

Lower Limb Balance Rehabilitation of Post-stroke Patients Us...

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IEEE International Symposium on Mixed and Augmented reality Workshops (ISMARW)

作者： Shuwei Chen Ben Hu Yang Gao Zhiping Liao Jianhua Li Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Ningbo University State Key Laboratory of Virtual Reality Technology and Systems Research Institute of Frontier Science Beihang University Sir Run Run Shaw Hospital Zhejiang University School of Medicine

ISBN: (数字)9781728176758

ISBN: (纸本)9781728176765

Augmented/virtual reality applications can provide immersive and interactive virtual environment for motor rehabilitation using the collaborative stimulations of multiple sensory channels such as sight, hearing, and movement, enhance the rehabilitation effect through repetitions, feedbacks, and encouragement. In this paper, we propose an evaluating and training integrated application for the rehabilitation of patients with lower limb balance disorder. The AR-based evaluation module visualizes the limits of lower limbs patients' balance abilities and provides quantitative data to their therapists, then rehabilitation therapists can customize personalized VR training games accordingly.

关键词： Training Human computer interaction virtual environments Medical treatment Games Usability Augmented reality

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Nonsingular field-of-view constrained cooperative guidance with improved time-to-go estimation

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Chinese Journal of Aeronautics 2025年

作者： Xiaofei YANG Yunjie WU Weibo XIA Xiaodong LIU Haibin DUAN School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beĳing Aerospace Automatic Control Institute Beĳing 100854 China

This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View (FOV) constraints. First, a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance (FPNG) law. Then, the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time. A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command. Moreover, the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point. Finally, several numerical simulations are conducted, and the results verify the effectiveness, robustness, and advantages of the proposed cooperative guidance law.

关键词： Flight vehicles Cooperative guidance Field-of-view constraints Time-to-go estimation Predefined-time convergence

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Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference

arXiv

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arXiv 2021年

作者： Niu, Guanglin Li, Bo Zhang, Yongfei Pu, Shiliang Institute of Artificial Intelligence Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Beijing Key Laboratory of Digital Media Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hikvision Research Institute Hangzhou China

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge graph

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Cloud sphere: A 3D Shape representation via progressive deformation

arXiv

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arXiv 2021年

作者： Wang, Zongji Liu, Yunfei Lu, Feng Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China

In the area of 3D shape analysis, the geometric properties of a shape have long been studied. Instead of directly extracting representative features using expertdesigned descriptors or end-to-end deep neural networks, this paper is dedicated to discovering distinctive information from the shape formation process. Concretely, a spherical point cloud served as the template is progressively deformed to fit the target shape in a coarse-to-fine manner. During the shape formation process, several checkpoints are inserted to facilitate recording and investigating the intermediate stages. For each stage, the offset field is evaluated as a stage-aware description. The summation of the offsets throughout the shape formation process can completely define the target shape in terms of geometry. In this perspective, one can derive the point-wise shape correspondence from the template inexpensively, which benefits various graphic applications. In this paper, the Progressive Deformation-based Auto-Encoder (PDAE) is proposed to learn the stage-aware description through a coarseto-fine shape fitting task. Experimental results show that the proposed PDAE has the ability to reconstruct 3D shapes with high fidelity, and consistent topology is preserved in the multi-stage deformation process. Addi-tional applications based on the stage-aware description are performed, demonstrating its universality. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

VS-Net: A Voxel Encoding and Sparse Convolution Embedded Net...

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IEEE International Conference on robotics and Biomimetics

作者： Meng Liu Jianwei Niu Yu Liu China North Artificial Intelligence and Innovation Research Institute Beijing China China North Vehicle Research Institute Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Software Development Envi-ronment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781665481106

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios.

关键词： Point cloud compression Laser radar Three-dimensional displays Convolution Object detection Detectors Feature extraction

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Counting dense objects in remote sensing images

arXiv

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arXiv 2020年

作者： Gao, Guangshuai Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Hangzhou Innovation Institute Beihang University Hangzhou China

Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Bidirectional Optimization Coupled Ligh tweight Net works for Efficient and Robust Multi-Person 2D Pose Estimation

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Journal of Computer Science & technology 2019年第3期34卷 522-536页

作者： Shnai Li Zheng Fang Wen-Feng Song Ai-Min Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Beihang University Qingdao Research Institute Qingdao 266000China Department of Computer Science Stony Brook UniversityStony Brook 11790U.S.A.

For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain *** principle,bottom-up methods are superior to top-down methods in efficiency,but they perform worse in *** make full use of their respective advantages,in this paper we design a novel bidirectional optimization coupled lightweight network(BOCLN)architecture for efficient,robust,and general-purpose multi-person 2D(2-dimensional)pose estimation from natural *** the BOCLN framework,the bottom-up network focuses oil global features,while the top-down net work places emphasis on det ailed *** entire framework shares global features along the bottom-up data stream,while the top-down data stream aims to accelerate the accurate pose *** particular,to exploit the priors of human joints'relationship,we propose a probability limb heat map to represent the spatial context of the joints and guide the overall pose skeleton prediction,so that each person's pose estimation in cluttered scenes(involving crowd)could be as accurate and robust as ***,benefiting from the novel BOCLN architecture,the tinie-consuming refinement procedure could be much simplified to an efficient lightweight *** experiments and evaluations on public benchmarks have confirmed that our new method is more efficient and robust,yet still attain competitive accuracy performance compared with the state-of-the-art *** BOCLN shows even greater promise in online applications.

关键词： bidirectional optimization computer vision deep learning probability limb heat map 2D multi-person pose estimation

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Object Pose Estimation for Robotic Grasping based on Multi-view keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots Image color analysis Robot kinematics Pose estimation Robot vision systems

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