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An energy constraint position-based dynamics with corrected SPH kernel

Science China(Information Sciences) 2023年第1期66卷 144-157页

作者： Wei CAO Luan LYU Zhixin YANG Enhua WU College of Computer Science and Technology China University of Petroleum Faculty of Science and Technology University of Macau State Key Laboratory of Internet of Things for Smart City University of Macau State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Guangzhou Greater Bay Area Virtual Reality Research Institute

We introduce an improved position-based dynamics method with corrected smoothed particle hydrodynamics(SPH) kernel to simulate deformable solids. Using a strain energy constraint that follows the continuum mechanics, the method can maintain the efficiency and stability of the position-based approach while improving the physical plausibility of the simulation. We can easily simulate the behavior of anisotropic and plastic materials because the method is based on physics. Unlike the previous position-based simulations of continuous materials, we use weakly structured particles to discretize the model for the convenience of deformable object cutting. In this case, a corrected SPH kernel function is adopted to measure the deformation gradient and calculate the strain energy on each particle. We also propose a solution for the interparticle inversion and penetration in large deformation. To perform complex interaction scenarios, we provide a simple method for collision detection. We demonstrate the flexibility, efficiency, and robustness of the proposed method by simulating various scenes, including anisotropic elastic deformation, plastic deformation, model cutting, and large-scale elastic collision.

关键词： position-based dynamics deformable solids continuum mechanics

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DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection 41

DSD-DA: Distillation-based Source Debiasing for Domain Adapt...

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41st International Conference on Machine Learning, ICML 2024

作者： Feng, Yongchao Li, Shiwei Gao, Yingjie Huang, Ziyue Zhang, Yanan Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhongguancun Laboratory Beijing China

Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works. Copyright 2024 by the author(s)

关键词： Domain Knowledge

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High-Precision Localization of Mobile Robot Based on Particle Filters Combined with Triangle Matching 7th

High-Precision Localization of Mobile Robot Based on Parti...

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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Zhou, Huaidong Tuo, Wanchen Chou, Wusheng Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819906161

Robot location is a fundamental technology in the field of autonomous robotics and has been widely studied. However, high-precision localization of the mobile robot is still a serious challenge. To investigate this point further, this paper proposes a method to improve the autonomous robot localization accuracy, which adjusts the number of particles in the AMCL algorithm by fusing the information of LRL. LRL data is used to improve the localization accuracy of mobile platforms and prevent kidnapped robot problems. The algorithm is conducted and verified in a real robot system. The experiment results show that the proposed method can achieve high-precision locations in real-world scenarios, and verify the effectiveness of the improved localization system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Glass Wool Defect Detection Using an Improved YOLOv5

Glass Wool Defect Detection Using an Improved YOLOv5

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Jin, Yizhou Lu, Yu Zhou, Gang Liu, Qingjie Wang, Yunhong Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beihang University Hangzhou Innovation Institute China

ISBN: (纸本)9798350302493

Glass wool defect detection is a key part of product quality assessment in the glass wool production process, yet few studies have been reported in this area. We propose a glass wool defect dataset named GWD, and also use the YOLOv5s model embedded in the GSConv and the CBAM modules for both Gap and Glueless defects in this dataset. The experimental results show that the performance of the improved YOLOv5s on the GWD dataset is superior to other compared methods and achieves a relatively good level on other publicly available datasets. Compared to the vanilla YOLOv5s, the mAP50 increased by 3.7% to 84.1%, the recall increased by 4.2% to 84.4%, and the number of parameters decreased by 0.42 MB to 6.27 MB of the improved YOLOv5s model on the GWD dataset. Speed-wisely, the improved YOLOv5s achieves a 97 FPS on RTX 2080Ti, thus making it practical to be applied in the industry of glass wool defect detection. The research on the GWD dataset is likely to contribute to breakthroughs in research on other datasets of the same type as well. The GWD dataset can be obtained by contacting us via email. © 2023 IEEE.

关键词： Defects

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RMGNet: The Progressive Relationship-Mining Graph Neural Network for Text-to-image Person Re-identification

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5749-5761页

作者： Zhang, Xin Liu, Kun Wang, Xinwang Zhou, Zhong Chen, Haiyong Hebei University of Technology School of Artificial Intelligence Tianjin300130 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Southeast University School of Instrument Science and Engineering Jiangsu Nanjing210018 China Wuxi Institute of Technology School of Integrated Circuit Wuxi214121 China

The Text-to-Image Person Re-identification (TI-ReID) task objective is to precisely identify the person's images with the textual description of the person. The mainstream research methods focus on cross-modal aligning local features, and overlook the learning of intra-modal and cross-modal relationships between different features. This renders the person features lacking in high-level semantic information. To resolve such issues, we propose the Progressive Relationship-Mining Graph Network (RMGNet), including the Intra-Modal Relationship-Mining (IMRM) and the Cross-Modal Relationship-Mining (CMRM) module. These modules are employed to model and mine semantic relationship information among different features. Specifically, the IMRM module models and mines the high-level semantic interrelationships inherent in the image and text features. The CMRM module introduces the nearest neighbor method to model cross-modal semantic relationships to enhance the cross-modal semantic correspondence capabilities of person features. On this basis, we design the Adaptive Corner Center (Acc) loss and the Coarse-to-Fine Learning (C2FL) strategy. These ensure the network receives consistent and effective metric learning supervision throughout the entirety of the training process. To validate the efficacy of the proposed method, extensive experiments are conducted on three prevalent datasets: CHUK-PEDES, ICFC-PEDES, and RSTPReid. The achieved mAP of 70.59%, 41.62%, and 49.58% surpassed those current state-of-the-art methods. © 2025 IEEE.

关键词： Graph neural networks

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VRCK: A virtual reality Teaching System based on Generation and Editing of Constrained keyframes 9

VRCK: A Virtual Reality Teaching System based on Generation ...

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9th International Conference on virtual reality, ICVR 2023

作者： Wang, Kaiyi Zhao, Yongjia Dai, Shuling Sun, Ruize Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Jiangxi Research Institute Beihang University Jiangxi China

ISBN: (纸本)9798350345810

virtual reality (VR) demonstration is safe, convenient, low-cost, and has easy access to learning data. keyframes have been extensively used in learning from demonstration (LfD) to teach robots skills, especially when the skills need to be further updated and adapted. However, few works use keyframe-based methods in VR demonstrations to combine the advantages of both. Besides, human annotations and multiple demonstrations are always required to generate keyframe models, which causes problems of large demonstration workload, poor interpretability, and low generalization performance. To tackle the issues mentioned above, we propose VRCK: a VR teaching system based on generation and editing of constrained keyframes. In this system, only one demonstration is needed to generate the skill keyframes, path keyframes and additional keyframes. Constraints can be generated automatically using object detection and binocular localization algorithms. During the VR teaching process, users can visualize keyframes and constraints, and use UI to bind and edit them at any time. Finally, three task cases and a physical experiment are provided to validate our system and illustrate how it enables intratask generalization. © 2023 IEEE.

关键词： Demonstrations

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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Estimating the Pose of Irregular Surface Contact Based on Multi-colliders Collision Information and Nonlinear Neural Network in virtual Environment 7th

Estimating the Pose of Irregular Surface Contact Based on Mu...

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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Sun, Ruize Zhao, Yongjia State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Jiangxi Research Institute Beihang University Jiangxi Beijing China

ISBN: (纸本)9789819906161

The current robotic arm assembly mainly relies on vision to provide behavioral guidance. However, there are a large number of assembly tasks that cannot be completed by vision alone or have insufficient precision, such as the snap-fit of the Flexible Printed Cable and the insertion and removal of multi-pin plugs. These tasks require the guidance of force information, which we call haptics, generated during assembly. However, the current force simulation tools are difficult to achieve real-time accurate simulation, and usually occupy a lot of computational cost. In this paper, a method is proposed, which uses the collision system of Unity to model the local force on the contact surface, designs the collider array according to the contour structure of the surface. To verify the effectiveness of this representation, we train a simple network to estimate the attitude of irregular surface contact. we use the collected local force information as a feature. The position and pose of the head are used as the output, and a nonlinear neural network is trained, which can estimate the position and pose of the male head based on the collected features. It has been verified that the estimation accuracy of most of the outputs exceeds 95%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： virtual reality

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BISVP: Building Footprint Extraction Via Bidirectional Serialized Vertex Prediction 48

BISVP: Building Footprint Extraction Via Bidirectional Seria...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Mingming Du, Ye Hu, Zhenghui Liu, Qingjie Wang, Yunhong Beihang University Hangzhou Innovation Institute China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9781728163277

Extracting building footprints from remote sensing images has been attracting extensive attention recently. Dominant approaches address this challenging problem by generating vectorized building masks with cumbersome refinement stages, which limits the application of such methods. In this paper, we introduce a new refinement-free and end-to-end building footprint extraction method, which is conceptually intuitive, simple, and effective. Our method, termed as BiSVP, represents a building instance with ordered vertices and formulates the building footprint extraction as predicting the serialized vertices directly in a bidirectional fashion. Moreover, we propose a cross-scale feature fusion (CSFF) module to facilitate high resolution and rich semantic feature learning, which is essential for the dense building vertex prediction task. Without bells and whistles, our BiSVP outperforms state-of-the-art methods by considerable margins on three building instance segmentation benchmarks, clearly demonstrating its superiority. The code and datasets will be made public available. © 2023 IEEE.

关键词： Extraction

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