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Time-sequential color code division multiplexing holographic display with metasurface

Opto-Electronic Advances 2023年第8期6卷 8-16页

作者： Xin Li Qinmiao Chen Xue Zhang Ruizhe Zhao Shumin Xiao Yongtian Wang Lingling Huang Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China MoE Key Laboratory of Photoelectronic Imaging Technology and System and MIIT Key Laboratory of Photonics Information TechnologySchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China State Key Laboratory on Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemHarbin Institute of Technology(Shenzhen)Shenzhen 518055China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150080China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Color metasurface holograms are powerful and versatile platforms for modulating the amplitude,phase,polarization,and other properties of light at multiple operating ***,the current color metasurface holography can only realize static *** this study,we propose and demonstrate a multiplexing metasurface technique combined with multiwavelength code-division multiplexing(CDM)to realize dynamic *** code references are utilized to record information within a single metasurface and increase the information capacity and security for anticracks.A total of 48 monochrome images consisting of pure color characters and multilevel color video frames were reconstructed in dual polarization channels of the birefringent metasurface to exhibit high information density,and a video was displayed via sequential illumination of the corresponding code patterns to verify the ability of dynamic *** approach demonstrates significant application potential in optical data storage,optical encryption,multiwavelengthversatile diffractive optical elements,and stimulated emission depletion microscopy.

关键词： metasurface color holography dynamic display code division multiplexing

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Visual Explanation of Object Detectors via Saliency Maps

Visual Explanation of Object Detectors via Saliency Maps

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jin Xiao Wenrui Liu Xiaoguang Hu Hao Jiang Weipeng Wang Li Yahang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beihang University Beijing China The Third Academy of CASIC Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

In recent years, rapid progress of deep learning has resulted in object detection algorithms based on deep neural networks nearly completely replacing traditional methods. This shift has led to significant improvements in both accuracy and speed of object detection. However, deep neural networks that support decision-making inobject detection are complex black boxes with hidden internal logic and operations. This lack of transparency results in deep learning models being unexplainable, which limits their practical deployment. In this paper, we analyze the current state and future trends of interpretable technology in object detection and propose an effective object detection interpretable algorithm. This algorithm aims to explain the underlying logic of AI decision-making and provide support for the high performance and credibility of object detection algorithms based on deep neural networks through quantitative evaluation indicators.

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Edge-Preserving Depth Map Super-Resolution with Intensity Guidance

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Journal of Beijing Institute of technology 2019年第1期28卷 51-56页

作者： Xiaochuan Wang Xiaohui Liang State Key Laboratory of Virtual Reality Technology and System Beihang University

Depth discontinuity edge affects the visual quality of synthesized images in 3D image ***,it suffers from accuracy degradation when up-sampled from low-resolution depth maps,especially at large scaling *** preserve the accuracy of depth discontinuity,a novel joint bilateral depth super-resolution with intensity guidance method is ***,the fast local intensity classification is exploited to estimate depth coefficients in joint bilateral up-sampling for depth maps,so as to eliminate depth discontinuity edge ***,the proposed method is accelerated on graphic processing units(GPUs)to meet the requirement of realtime *** demonstrate that our method can preserve the accuracy of depth discontinuity edges after super resolution,leveraging the visual quality of synthesized image in 3D image warping.

关键词： super resolution depth map joint bilateral filter 3D image warping synthesized image

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Simulating the Dilution of Sulfuric Acid on Mixed reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

引用

8th International Conference on virtual reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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An Effective Two-stage Training Paradigm Detector for Small Dataset

arXiv

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arXiv 2023年

作者： Wang, Zheng Xie, Dong Wang, Hanzhi Tian, Jiang AI Lab Lenovo Research Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Yale University New HavenCT United States

Learning from the limited amount of labeled data to the pre-train model has always been viewed as a challenging task. In this report, an effective and robust solution, the two-stage training paradigm YOLOv8 detector (TP-YOLOv8), is designed for the object detection track in VIPriors Challenge 2023. First, the backbone of YOLOv8 is pre-trained as the encoder using the masked image modeling technique. Then the detector is fine-tuned with elaborate augmentations. During the test stage, test-time augmentation (TTA) is used to enhance each model, and weighted box fusion (WBF) is implemented to further boost the performance. With the well-designed structure, our approach has achieved 30.4% average precision from 0.50 to 0.95 on the DelftBikes test set, ranking 4th on the leaderboard. © 2023, CC BY.

关键词： Object detection

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Rethinking ReID: Multi-Feature Fusion Person Re-identification Based on Orientation Constraints

Rethinking ReID: Multi-Feature Fusion Person Re-identificati...

引用

International Conference on Pattern Recognition

作者： Mingjing Ai Guozhi Shan Bo Liu Tianyang Liu State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9781728188089;9781728188096

Person re-identification (ReID) aims to identify the specific pedestrian in a series of images or videos. Recently, ReID is receiving more and more attention in the fields of computer vision research and application like intelligent security. One major issue downgrading the ReID model performance lies in that various subjects in the same body orientations look too similar to distinguish by the model, while the same subject viewed in different orientations looks rather different. However, most of the current studies do not particularly differentiate pedestrians in orientation when designing the network, so we rethink this problem particularly from the perspective of person orientation and propose a new network structure by including two branches: one handling samples with the same body orientations and the other handling samples with different body orientations. Correspondingly, we also propose an orientation classifier that can accurately distinguish the orientation of each person. At the same time, the three-part loss functions are introduced for orientation constraint and combined to optimize the network simultaneously. Also, we use global and local features int the training stage in order to make use of multi-level information. Therefore, our network can derive its efficacy from orientation constraints and multiple features. Experiments show that our method not only has competitive performance on multiple datasets, but also can let retrieval results aligned with the orientation of the query sample rank higher, which may have great potential in the practical applications.

关键词： Training Computer vision Computational modeling Focusing Pattern recognition Security Videos

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CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment

arXiv

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arXiv 2024年

作者： Zhou, Kanglei Li, Junlin Cai, Ruizhi Wang, Liyuan Zhang, Xingxing Liang, Xiaohui Department of Computer Science and Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China China Three Gorges University China

Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. However, this common strategy yields suboptimal results due to the inherent struggle of these backbones to capture the subtle cues essential for AQA. Moreover, fine-tuning on smaller datasets risks overfitting. To address these issues, we propose Coarse-to-Fine Instruction Alignment (CoFInAl). Inspired by recent advances in large language model tuning, CoFInAl aligns AQA with broader pre-trained tasks by reformulating it as a coarse-to-fine classification task. Initially, it learns grade prototypes for coarse assessment and then utilizes fixed sub-grade prototypes for fine-grained assessment. This hierarchical approach mirrors the judging process, enhancing interpretability within the AQA framework. Experimental results on two long-term AQA datasets demonstrate CoFInAl achieves state-of-the-art performance with significant correlation gains of 5.49% and 3.55% on Rhythmic Gymnastics and Fis-V, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Cumuliform Cloud Animation Control Based on Natural Images

Cumuliform Cloud Animation Control Based on Natural Images

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2020 International Conference on virtual reality and Visualization, ICVRV 2020

作者： Zhang, Zili Zhang, Yiming Li, Yan Liang, Xiaohui State Key Laboratory of Virtual Reality System and Technology Beihang University Shijiazhuang University Beijing Xiaomi Mobile Software Co. Ltd. Equipment Simulation Training Center Shijiazhuang Campus of Arm Engineering University State Key Laboratory of Virtual Reality System and Technology Beihang University

ISBN: (纸本)9780738142524

Cloud is an important element in synthetic outdoor scenes. Many methods have been developed for simulating realistic clouds. The image-based cloud modeling methods can estimate cloud shape similar to the input image. However, simulating cloud evolution among multiple specified shapes remains a challenging task. This paper presents a method to simulate the evolution of cloud between two given natural images, which are treated as the initial and target shapes, respectively. Atmospheric hydrodynamics combined with particle system is used to generate clouds physical movement. Then, taking the target shape as constraint, the driving forces are calculated and the control over the cloud movement is realized. Finally, we apply the PBF approach to ensure the cloud's incompressibility. The experiment results show that the proposed method can effectively simulate cloud evolution between discrepant initial and target shapes. © 2020 IEEE.

关键词： Animation

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Will you ever become popular? Learning to predict virality of dance clips

arXiv

引用

arXiv 2021年

作者： Wang, Jiahao Wang, Yunhong Weng, Nina Chai, Tianrui Li, Annan Zhang, Faxi Yu, Sansi State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China Tencent Shenzhen China

Dance challenges are going viral in video communities like TikTok nowadays. Once a challenge becomes popular, thousands of short-form videos will be uploaded in merely a couple of days. Therefore, virality prediction from dance challenges is of great commercial value and has a wide range of applications, such as smart recommendation and popularity promotion. In this paper, a novel multi-modal framework which integrates skeletal, holistic appearance, facial and scenic cues is proposed for comprehensive dance virality prediction. To model body movements, we propose a pyramidal skeleton graph convolutional network (PSGCN) which hierarchically refines spatio-temporal skeleton graphs. Meanwhile, we introduce a relational temporal convolutional network (RTCN) to exploit appearance dynamics with non-local temporal relations. An attentive fusion approach is finally proposed to adaptively aggregate predictions from different modalities. To validate our method, we introduce a large-scale viral dance video (VDV) dataset, which contains over 4,000 dance clips of eight viral dance challenges. Extensive experiments on the VDV dataset well demonstrate the effectiveness of our approach. Furthermore, we show that short video applications like multi-dimensional recommendation and action feedback can be derived from our model. © 2021, CC BY.

关键词： Forecasting

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Variable Admittance Force Feedback Device for Space Teleoperation and its Human-Robot Interaction Stability

SSRN

引用

SSRN 2022年

作者： Wang, Yuhang Liu, Guanyang Huang, Ge Wang, Yi School of Mechanical Engineering and Automation Beihang University Beijing100083 China State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing100083 China

In teleoperation, a force feedback device is a medium to build a transparent interaction environment between a human and a remote robotic arm. Using force feedback devices, the users can operate the remote robotic arm intuitively and perceive remote interaction through the force channel, just as if they are in the remote environment. Compared with impedance force feedback devices, admittance devices have the advantages of large feedback output, high stiffness, high reverse driving performance and flexible structure, which are more suitable for heavy-duty and large-size robotic arm teleoperation. However, the control of admittance devices is relatively complex and has some inherent limitations such as the inability to recognize user intentions rapidly, instability from high-frequency oscillation, difficulty in achieving constant speed control, etc., and the application range is limited by the performance of force sensors. Admittance model parameter errors and physiological characteristics, such as human arm stiffness and force application fluctuations, are the root causes of these problems. The application of the fuzzy variable damping admittance model allows the device to identify the user's movement intention and give respond quickly and accurately. In this study, we established a human-robot interaction (HRI) system model and summarized the principles of the admittance parameter configuration of a stable system. For the device high-frequency oscillation instability caused by human arm stiffness, we propose an oscillation observation algorithm based on a scalable sliding window. By observing the force signal change characteristics, the algorithm can quickly detect the unstable behavior caused by human hands and perform oscillation reduction. To reduce the influence on upper limb uniform motion caused by fluctuating human physiological characteristics in force application, we proposed a constant velocity intention inference algorithm based on a velocity spherical co

关键词： Human robot interaction

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