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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Investigation of Biaxial Prestrain Influence on Bending-Under-Tension Springback 14th

Investigation of Biaxial Prestrain Influence on Bending-Unde...

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14th International Conference on technology of Plasticity, ICTP 2023

作者： Yang, Y. Baudouin, C. Chalal, H. Vincze, G. Balan, T. State Key Lab of Solidification Processing School of Materials Science and Engineering Northwestern Polytechnical University Xi’an710072 China Arts et Metiers Institute of Technology Université de Lorraine LCFC Metz57000 France Center for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago Aveiro3810-193 Portugal LASI – Intelligent Systems Associate Laboratory Guimarães4800-058 Portugal Arts et Metiers Institute of Technology Université de Lorraine LEM3 Metz57000 France

ISBN: (纸本)9783031409196

Springback notoriously depends on the strain-path history of the sheet material during forming. The bending-under-tension (BUT) test is used in this work for a discriminant characterization of springback in various conditions. In order to generate non-linear strain-paths, tensile prestrain is usually applied in the literature. In this work, an original biaxial prestrain device was designed and build. It allowed for the uniform prestrain of large samples of 500 mm diameter in DP600, up to 9.5% of strain. Subsequent samples for bending-under-tension were cut and tested at different combinations of tool radius and backforce. Classical models were used to describe the material’s behavior within finite element simulations of the BUT test. These experiments allowed for a discriminant comparison between the springback predictions of competing constitutive models. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bending-Under-Tension Biaxial prestrain DP600 Springback

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SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation

arXiv

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arXiv 2023年

作者： An, Xiaoqi Zhao, Lin Gong, Chen Wang, Nannan Wang, Di Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China

High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input images or fine-grained image tokens. However, this dense high-resolution representation brings a significant computational burden. In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?" Based on dynamic transformer models, we propose a framework that only uses Sparse High-resolution Representations for human Pose estimation (SHaRPose). In detail, SHaRPose consists of two stages. At the coarse stage, the relations between image regions and keypoints are dynamically mined while a coarse estimation is generated. Then, a quality predictor is applied to decide whether the coarse estimation results should be refined. At the fine stage, SHaRPose builds sparse high-resolution representations only on the regions related to the keypoints and provides refined high-precision human pose estimations. Extensive experiments demonstrate the outstanding performance of the proposed method. Specifically, compared to the state-of-the-art method ViTPose, our model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and 76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of 1.4× faster than ViTPose-Base. Code is availab.e at https://***/AnxQ/sharpose. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array for Fiber Bragg Grating Interrogation

A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array f...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Meng, Lingxin Dai, Pan Ban, Qilu Xu, Kaichuan Wang, Jiacheng Li, Zhen Wang, Feng Zeng, Jie Li, Shaobo Ma, Xiang Chen, Xiangfei Res. Center of Precision Photonics Integration and System Application of the Ministry of Education College of Engineering and Applied Sciences Nanjing University Key Lab. of Intelligent Optical Sensing and Manipulation of the Ministry of Education Engineering National Laboratory of Solid State Microstructures Nanjing210023 China Nanjing University of Aeronautics and Astronautics State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing210016 China The 54th Research Institute of China Electronics Technology Group Corporation Optical Communication Research and Development Center Shijiazhuang050081 China

ISBN: (纸本)9798350312614

We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. With the aid of our designed laser driving circuit system consists of the microcontroller unit (MCU), field-programmable gate array (FPGA) and negative metal-oxide-semiconductor field effect transistors (n-MOSFETs), the precise pulse driving current for the 4-channel wavelength-swept REC-DFB laser array can be generated and a fast tuning of 16.424 nm within 10 μs is achieved. To further investigate the tuning performance of the proposed wavelength-swept laser source, we then applied it to the fiber Bragg grating (FBG) interrogation system and four FBG temperature sensor arrays were successfully demodulated. The development of high-efficient optical sensing systems heavily relies on the significance of the ultrafast wavelength-swept laser source, which has the potential to greatly enhance FBG interrogator performance in the future. © 2023 IEEE.

关键词： DFB laser array fast wavelength-swept fiber Bragg grating interrogation REC technique

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Guofeng Zhang Suxu Cong Jian Yang Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFsfor the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos.

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression Computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding intelligent robots

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TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles

arXiv

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arXiv 2023年

作者： Ma, Yifeng Wang, Suzhen Ding, Yu Ma, Bowen Lv, Tangjie Fan, Changjie Hu, Zhipeng Deng, Zhidong Yu, Xin Li, Lincheng Department of Computer Science and Technology BNRist THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University China Fuxi AI Lab Netease Zhejiang Hangzhou China The School of Computer Science The University of Queensland Brisbane Australia

Audio-driven talking head generation has drawn growing attention. To produce talking head videos with desired facial expressions, previous methods rely on extra reference videos to provide expression information, which may be difficult to find and hence limits their usage. In this work, we propose TalkCLIP, a framework that can generate talking heads where the expressions are specified by natural language, hence allowing for specifying expressions more conveniently. To model the mapping from text to expressions, we first construct a text-video paired talking head dataset where each video has diverse text descriptions that depict both coarse-grained emotions and fine-grained facial movements. Leveraging the proposed dataset, we introduce a CLIP-based style encoder that projects natural language-based descriptions to the representations of expressions. TalkCLIP can even infer expressions for descriptions unseen during training. TalkCLIP can also use text to modulate expression intensity and edit expressions. Extensive experiments demonstrate that TalkCLIP achieves the advanced capability of generating photo-realistic talking heads with vivid facial expressions guided by text descriptions. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Air-to-Air Detection and Tracking of Non-Cooperative UAVs for 3D Reconstruction

Air-to-Air Detection and Tracking of Non-Cooperative UAVs fo...

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International Conference on Control and Automation (ICCA)

作者： Yang Liu Lele Xi Zhihao Sun Lele Zhang Wei Dong Maobin Lu Chen Chen Fang Deng State Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing P.R China School of Electrical Engineering Hebei University of Science and Technology Shijiazhuang Hebei P.R. China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

The monitoring and regulation of unauthorized, unresponsive, or uncommunicative non-cooperative UAVs is still one of the great challenges in airspace safety management. Therefore, in order to obtain the phenotypic information of these targets, we propose a 3D reconstruction system for non-cooperative UAVs based on air-to-air view image sequences. Specifically, the data-collecting UAV (pursuer) will detect, track, approach, and follow the non-cooperative UAV (target) at a fixed distance to automatically capture the image data. During the process, a lightweight object detection model and a Kalman filter-based tracking algorithm are used to recognize and continuously track the target UAV, and the boundary state constrained primitives and interval replanning are used to generate the accurate flight path, which in turn will control the pursuer UAV to approach the target UAV and follow it at a fixed distance. Finally, based on the implicit representation of the neural radiation field, the collected images will be used for 3D reconstruction and rendering of the non-cooperative UAV. In experiments, taking a quadrotor as the pursuer and a fixed-wing UAV as the target, we verify the feasibility of the proposed system in 3D reconstruction tasks.

关键词： Target tracking Three-dimensional displays Target recognition Atmospheric modeling Safety management Object detection Autonomous aerial vehicles

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Unsupervised Detailed Human Shape Estimation from Multi-view Color Images 11th

Unsupervised Detailed Human Shape Estimation from Multi-view...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zheng, Huayu Wang, Kangkan Li, Wei Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030873578

This paper presents a novel framework to estimate detailed human body shape from color images in an unsupervised manner. It is a challenging task due to factors such as variations in human shapes, occlusion, and cloth details. The existing methods are mainly supervised and require a large number of ground truth real training data which is usually hard to obtain. To solve this problem, we propose an unsupervised detailed human shape estimation method from multi-view color images. Specifically, we first predict the depth map for the source view through robust photometric consistency with different views. Then, we predict the initial SMPL model from the color image and refine it by an iterative error feedback regressor based on point clouds of the predicted depth map. Finally, the refined SMPL model is deformed to fit the details (i.e., clothes and faces) on the point clouds to recover the detailed human shapes which are represented by adding a set of offsets to the SMPL model. The experimental results on different dataset demonstrate that our method outperforms the state-of-the-art methods and achieves higher reconstruction accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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A New Collision Detection Algorithm for Vascular Interventional Surgery Simulation Training System 18

A New Collision Detection Algorithm for Vascular Interventio...

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18th IEEE International Conference on Mechatronics and Automation, ICMA 2021

作者： Guo, Jian Wang, Zhentao Guo, Shuxiang Tianjin University of Technology Tianjin Key Lab. for Contr. Theor. Applic. in Complicated Systems and Intelligent Robot Laboratory Binshui Xidao 391 Tianjin China Departinent of Intelligent Mechanical Systems Engineering Faculty of Engineering Kagawa University 2217-20 Hayashi-cho Takamatsu761-0396 Japan

ISBN: (纸本)9781665441001

In virtual vascular interventional surgery, collision detection between surgical instruments and human soft tissue is the basis for deformation and pressure calculation, and it is also a prerequisite for model cutting. Due to the high calculation and complexity in the virtual vascular surgery simulation process, the existing collision detection algorithms can not satisfy the requirements of real-time and accuracy during surgery. Drawing on the deficiencies and improvements of the soft tissue object collision detection algorithm, and combining the unique characteristics of the soft tissue deformation model, a new soft tissue hybrid collision detection algorithm is proposed. Through the two stages of rough detection and precise detection, a large number of redundant calculations in the original collision detection algorithm are omitted, and the response time of collision detection is greatly reduced. Experiments are compared with the other two algorithms to verify that its real-time performance and accuracy have been improved. © 2021 IEEE.

关键词： Tissue

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