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Multi-task learning andjoint refinement between camera localization and object detection

Computational Visual Media 2024年第5期10卷 993-1011页

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518052China Qingdao Research Institute of Beihang University Qingdao 266104China School of Computer Science and Technology Shandong UniversityQingdaoChina

Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.

关键词： visual localization object detection joint optimization multi-task learning

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Further results on consensus for coupled swarm systems 18

Further results on consensus for coupled swarm systems

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Wang, Shaojie Xiao, Jin Hu, Xiaoguang Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9798350312201

The motion of the swarm system can be composed of the absolute motion of the swarm as a whole and the relative motion between nodes. Especially for nonlinear swarm systems, their absolute motion remains unknown. This paper considers a synchronization model for continuous-time swarm systems, which can be viewed as a generalized version of the consensus model for multi-agent systems, where node dynamics can be linear or nonlinear, and the coupling function of the state between nodes can also be linear or nonlinear. This paper is dedicated to answer what the specific form of the synchronization function (i.e., the absolute motion of the synchronized swarm system) is, and how to determine the contribution rate of the nodes in the network to the synchronization function. We leverage the state decomposition method to determine the synchronization function. Specifically, the paper illustrates the relationship between the synchronization function and the solution of the isolated node. © 2023 IEEE.

关键词： Synchronization

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Research on Electrical System Modeling of Aerostat Based on Harmony-SE 18

Research on Electrical System Modeling of Aerostat Based on ...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Pan, Xiang Xiao, Jin Hu, Xiaoguang Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9798350312201

In order to solve the problems of low efficiency of traditional model-based methods in the design and modeling of the electrical system of aerostat. Aiming at the design and modeling problems of aerostat electrical system, which is a typical multidisciplinary complex system, this paper firstly proposes a design method based on functional flow, state flow and information flow on the basis of the Harmony-SE method. The information flow is regarded as the element connecting between states and functions, and the requirements, structure and behavior model of the electrical system of aerostat is constructed. Then we put forward the method of building a structural model around the information flow and mapping the structural model to the integration of FMU model. We realized the physical modeling and model integration of part of the electrical system of aerostat, and finally completed the design and modeling of four parts of the electrical system of aerostat from requirements, functions, logic and physics. This method can improve the efficiency of multidisciplinary complex system design and modeling by optimizing the context use case repeated analysis and function design redundancy when the traditional model-based method is applied to multidisciplinary complex system design. © 2023 IEEE.

关键词： Efficiency

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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The Multi-domain Modeling and Optimization of Aircraft Electrical System Based on Modelica 18

The Multi-domain Modeling and Optimization of Aircraft Elect...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xiao, Jin Li, Yue Hu, Xiaoguang Reality Technology and Systems Beihang University State Key Laboratory of Virtual Beijing China

ISBN: (纸本)9798350312201

Aircraft electrical system includes key equipment such as battery, step-down circuit, filter, motor and so on. It is a comprehensive system integrating machinery, electronics, control, thermodynamics and other fields. However, many current modeling and simulation methods are aimed at specific problems in a single domain or subsystem, and it is difficult to consider the coupling relationship between different domains. To solve the above problems, this paper uses the multi-domain physical system modeling language Modelica to establish the aircraft electrical system model library on the simulation platform Mworks, and simulates the electrical characteristics of each model, some parameters are optimized. The results show that the multi-domain modeling of aircraft electrical system based on Modelica language can realize the simulation results close to the actual, and the optimization of related parameters can effectively improve the system performance and improve the design efficiency. © 2023 IEEE.

关键词： Simulation platform

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Wind Estimation for Multirotor UAV Control based on Surface Pressure Distribution 5

Wind Estimation for Multirotor UAV Control based on Surface ...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Yu, Junhao Chou, Wusheng Rong, Yongfeng School of Mechanical Engineering and Automation Beihang University Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China

ISBN: (纸本)9798350386943

The multirotor unmanned aerial vehicles (UAVs) are currently commonly used for various outdoor operations;However, gusts of wind may affect their stable control, and even lead to safety accidents. The common approach is to install wind sensors to obtain wind information for gust-resistant control, but traditional sensors are large, heavy, and slow to respond, making them unsuitable for small UAVs. In this paper, a compact, lightweight, fast-responsive wind estimation module is designed. When wind passes over the module, the surface pressure distribution information will be captured by the pressure sensors, and an artificial neural network is proposed to estimate the wind speed and direction. The effectiveness of the module was verified by testing it on a small UAV in the 5m/s wind field, achieving 4.09° direction estimation error and 0.34m/s speed estimation error. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Text-prompt Camouflaged Instance Segmentation with Graduated Camouflage Learning 24

Text-prompt Camouflaged Instance Segmentation with Graduated...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Zhentao Xia, Changqun Qiao, Shengye Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798400706868

Camouflaged instance segmentation (CIS) aims to detect and segment objects blending with their surroundings. While existing CIS methods rely heavily on fully-supervised training with massive precisely annotated data, consuming considerable annotation efforts yet struggling to segment highly camouflaged objects accurately. Despite their visual similarity to the background, camouflaged objects differ semantically. Since text associated with images offers explicit semantic cues to underscore this difference, we propose a novel approach: the first Text-Prompt based weakly-supervised camouflaged instance segmentation method named TPNet, leveraging semantic distinctions for effective segmentation. TPNet operates in two stages: pseudo mask generation and a self-training process. In the first stage, we align text prompts with images using a language-image model to obtain region proposals containing camouflaged instances. A Semantic-Spatial Iterative Fusion module is designed to assimilate spatial information with semantic insights, iteratively refining pseudo mask. In the second stage, Graduated Camouflage Learning, a self-training strategy, sequences training from simple to complex images based on camouflage levels, facilitating an effective learning gradient. Through the collaboration of the dual phases, our method offers a comprehensive experiment on two common benchmark and demonstrates a significant advancement, delivering a novel solution that bridges the gap between weak-supervised and high camouflaged instance segmentation. © 2024 Owner/Author.

关键词： Semantic Segmentation

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A Metal Surface Damage Recognition Method For Augmented reality Assisted Maintenance systems

A Metal Surface Damage Recognition Method For Augmented Real...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Wu, Hongduo Zhou, Dong Guo, Ziyue Wang, Yan Zhou, Qidi School of Reliability and System Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798350386097

The small damages such as cracks and scratches on the surface of aerospace products pose a serious threat to the safety of life and property, and manual visual inspection is prone to omissions, leaving great safety hazards. Using augmented reality (AR) assisted maintenance systems to assist visual inspection is one of the effective solutions. However, the limitations of computing power in augmented reality devices and the real-time requirements of augmented reality pose significant challenges to small-scale object detection algorithms. Therefore, this paper proposed a metal surface damage recognition method for augmented reality assisted maintenance system. Firstly, for the appearance characteristics of surface damage in the steel image database NEU-CLS, the histogram equalization was employed for image enhancement to improve image quality. Afterwards, a SURF + K-means + Bag-of-Features + the-number-of-feature-points feature extraction and dimensionality reduction method was proposed to improve recognition efficiency while ensuring the robustness of the method. Finally, adaptive boosting learning framework was utilized to construct a surface damage recognition model which has good accuracy and efficiency for common metal surface damages. © 2024 IEEE.

关键词： Dimensionality reduction

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A Conditional Diffusion Model for 3D Human Pose Estimation 4

A Conditional Diffusion Model for 3D Human Pose Estimation

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4th International Conference on Consumer Electronics and Computer engineering, ICCECE 2024

作者： Chen, Zhangmeng Dai, Ju Pan, Junjun Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350384062

Monocular 3D human pose estimation is a highly challenging task due to inherent depth ambiguity and occlusion. In response to these challenges, many previous works have utilized temporal information to enhance the richness of information. However, such methods can only derive a specific 3D pose from a single image, which fails to address ambiguity. Fortunately, denoising diffusion probabilistic models have recently achieved success in various computer vision tasks (e.g., generating images and image segmentation). Diffusion-based methods produce a set of possible result for each image. Inspired by this, we explore a novel Conditional Diffusion-based 3D human Pose estimation framework (CDiffPose) that formulates 3D human pose estimation as a 2D keypoints-guided 3D pose synthesis problem. We utilize a pre-trained model to represent 2D keypoints information as conditioning information. Subsequently, we inject the conditional information into the denoising model. Our denoising model (Denoiser) adopts a cascaded approach involving GCN and Transformers. Finally, the model generates multiple 3D candidates poses for a single 2D keypoint to alleviate the inherent depth ambiguity. Extensive experiments are conducted on Human3.6M benchmark, our proposed method demonstrates competitive performance. © 2024 IEEE.

关键词： Diffusion

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Robust and efficient edge-based visual odometry

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Computational Visual Media 2022年第3期8卷 467-481页

作者： Feihu Yan Zhaoxin Li Zhong Zhou State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Institute of Computing Technology Chinese Academy ofSciencesBeijing 100190China

Visual odometry,which aims to estimate relative camera motion between sequential video frames,has been widely used in the fields of augmented reality,virtual reality,and autonomous ***,it is still quite challenging for stateof-the-art approaches to handle low-texture *** this paper,we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera *** contrast to direct methods,we choose reprojection error to construct the optimization energy,which can effectively cope with illumination *** distance transform map built upon edge detection for each frame is used to improve tracking efficiency.A novel weighted edge alignment method together with sliding window optimization is proposed to further improve the *** on public datasets show that the method is comparable to stateof-the-art methods in terms of tracking accuracy,while being faster and more robust.

关键词： visual odometry(VO) edge structure distance transform low-texture

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