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FRPGS: Fast, Robust, and Photorealistic Monocular Dynamic Scene Reconstruction with Deformable 3D Gaussians

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Wan Pan, Xiao Lin, Jiaxin Lu, Ping Feng, Daquan Shi, Wenzhe Shenzhen University College of Electronics and Information Engineering Shenzhen518061 China Zhejiang University ReLER Lab CCAI Hangzhou310058 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Co. Ltd Shenzhen518052 China

Dynamic reconstruction technology presents significant promise for applications in visual and interactive fields. Current techniques utilizing 3D Gaussian Splatting show favorable results and fast reconstruction speed. However, as scene expanding, using individual Gaussian structure (i) leads to instability in large-scale dynamic reconstruction, marked by abrupt deformation, and (ii) the heuristic densification of individuals suffers significant redundancy. Tackling these issues, we propose a jointed Gaussian representation method named FRPGS, which learns the global information and the deformation using center Gaussians and generates the neural Gaussians around them for local detail. Specifically, FRPGS employs center Gaussians initialized from point clouds, which are learned with a deformation field for representing global relationships and dynamic motion over time. Then, for each center Gaussian, attribute networks generate neural Gaussians that move under the linked center Gaussian driving, thereby ensuring structural integrity during movement within this joint-based representation. Finally, to reduce Gaussian redundancy, a densification strategy is developed based on the average cumulative gradient of the associated neural Gaussians, imposing strict limits on the growing of center Gaussians without compromising accuracy. Additionally, we established a large-scale dynamic indoor dataset at the MuLong laboratory of ZTE Corporation. Evaluations demonstrate that FRPGS significantly outperforms state-of-the-art methods in both training efficiency and reconstruction quality, achieving over a 50% (up to 74%) improvement in efficiency on an RTX 4090. FRPGS also supports the 4K resolution reconstruction of 60 frames simultaneously. © 1991-2012 IEEE.

关键词： 3D reconstruction

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Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Hey! You Photographed My Screen without Approved: A Practica...

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2022 International Conference on networking and network applications, NaNA 2022

作者： Liu, Kui Wang, Bin Shi, Ziwei Chen, Jiadong Zhang, Zhiwei School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Network and Information Security Laboratory Hangzhou Hikvision Digital Technology Co. LTD. Hangzhou China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security. © 2022 IEEE.

关键词： Digital storage

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Service Decision Mechanism Based On Traffic Flow Prediction in the Internet of Vehicles

Service Decision Mechanism Based On Traffic Flow Prediction ...

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International Conference on Communication technology (ICCT)

作者： Yanzhe Hu Chenyuan Feng Wei Jiang Wenzhe Shi Daquan Feng Shenzhen Key Laboratory of Digital Creative Technology Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen University Shenzhen China College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation China

The service time of Road-Side Units (RSUs) for vehicle users will directly affect the Quality of Service (QoS) in the Internet of Vehicles (IoV), and it is related to the traffic flow and service decisions. Consequently, a sophisticated service decision mechanism based on the traffic flow prediction is necessary beforehand in order to optimize QoS. In this work, we propose a service decision based on traffic flow prediction to reduce the service waiting time of vehicles. Firstly, we propose a traffic flow prediction model based on deep learning. Convolutional Neural network (CNN) is used to capture spatial data in order to handle the complicated road network layout in metropolitan roads. First, to reveal people's travel patterns, temporal features are captured by two different models. While short-term features are captured from historical data by Auto-Encoder, daily period feature as well as weekly period feature are captured from bi-directional data by Bidirectional Long Short-Term Memory (Bi-LSTM). Second, by simulating the process of RSUs serving vehicles using a queuing theory model, we propose a service decision mechanism based on the anticipated traffic flow. The proposed service decision mechanism aims to reduce the average waiting time of vehicles for service. Particularly, the RSUs will ask the Macro Base Station (MBS) for assistance when there are too many vehicles traveling through the RSUs. Finally, we conduct comprehensive experiments using real data to evaluate the model performance, and it is demonstrated that our proposed scheme outperforms other benchmarks.

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A New Fourth-Order Sparse Array Generator Based on Sum-Difference Co-Array Analysis

A New Fourth-Order Sparse Array Generator Based on Sum-Diffe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodong Guo Hua Chen Hongguang Lin Wei Liu Qing Shen Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Information and Electronics Beijing Institute of Technology China

In this paper, based on sum-difference co-array analysis, a new fourth-order sparse array called sum-difference-FODC (SD-FODC) is proposed, allowing the construction of a fourth-order DCA with long consecutive lags using the continuous segments in the second-order DCA and SCA of the original array. It has a closed-form expression for sensor positions that can be generated by two arbitrary nonuniform linear arrays (NLAs) called generators. If the second-order SCA and DCA of the generators have long consecutive segments, the designed fourth-order sparse array can achieve a large number of uDOFs. Numerical results are provided to demonstrate the superior performance of the proposed design.

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A Dual-View Fusion network for Automatic Spinal keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dandan Zhou Lijun Guo Rong Zhang Xiuchao He Qiang Wang Jianhua Wang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Zhang Lijun Guo Lixin Ni Xiuchao He Jianhua Wang Rong Zhang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications.

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Privacy-Preserving Mobility-Aware Federated Collaborative Filtering Framework for Caching Prediction in Vehicular networks

Privacy-Preserving Mobility-Aware Federated Collaborative Fi...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Xinzhi Ouyang Chenyuan Feng Daquan Feng Howard H. Yang State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen Key Laboratory of Digital Creative Technology Shenzhen University Shenzhen China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Haining China

Recommendation algorithm can effectively reduce the difficulty of proactive edge caching prediction by excavating users’ preferences among the massive contents, which has drawn great attentions from both academia and industry. The effectiveness of prediction models depends on big data analysis of user information, however, traditional methods based on centralized learning become more and more impractical due to the growing concern on privacy data protection. Recently, implementing the recommendation algorithm in a federated learning (FL) manner has emerged as a promising approach. In an FL manner, users are allowed to keep their private data local and upload the model parameters learned by local training to the server for collaborative training. In this work, we propose a proactive caching prediction algorithm for mobile vehicle users based on differential privacy and federate learning. Our proposed algorithm not only predicts the popular contents with a strong protection for users’ private data, but also applies to large-scale networks with massive mobile users. In addition, we also investigate the impact of user mobility on the caching prediction accuracy, and propose an attention-based model aggregation mechanism, which assigns different aggregation weights to each vehicle user and edge server to mitigate the performance degradation caused by user movement. The results show that our proposed model can obtain high caching prediction accuracy and strong privacy protection level in vehicular networks.

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A Deep Learning Based Surface Current Generation Method for Scattering Modeling at Terahertz Band

A Deep Learning Based Surface Current Generation Method for ...

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European Conference on Antennas and Propagation, EuCAP

作者： Zhao Zhang Danping He Ke Guan Ben Chen Jianwu Dou Wei E.I. Sha Zhangdui Zhong School of Electronic and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China College of Information Science & Electronic Engineering Zhejiang University Zhejiang University Hangzhou China

As an essential candidate frequency band for next-generation communications, the terahertz band has gradually become a research hotspot. Due to wavelength comparable to surface undulation, scattering modeling becomes the core of characterizing the propagation mechanism. This paper proposes a deep learning model to learn the generation paradigm of surface currents (SC) under multiple input factors. Then, based on the generated SC, we use the dyadic Green's function to reconstruct the scattered electric field in the incidence plane and scattering hemisphere space. Experiments, among which the root mean square error of SC and electric field amplitude draws to 1.33~4.41 dB and 1.55~6.10 dB respectively, demonstrate the excellent generation ability of the proposed model and the feasibility of scattering reconstruction based on induced currents. This deterministic modeling approach can generate SC distribution and scattering beams of a specific surface more efficiently than full-wave simulation, providing a novel direction for scattering modeling.

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A Geometric Flow Perspective on Continuous-Depth networks

SSRN

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SSRN 2024年

作者： Chen, Jun Zhu, Tianyi Liu, Lina Jiang, Yunliang Liu, Yong Zhejiang Normal University Jinhua321004 China China Mobile Research Institute Beijing100032 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

The continuous-depth model, introduced by neural ordinary differential equations, have revived interest in exploring dynamic systems based on deep learning prototypes. The studies employed to investigate their theoretical properties mainly rely on Euclidean space, however, the geometric principle of general neural networks has been developed on the Riemannian manifold. Motivated by this open problem, we construct a formalized geometric theory of continuous-depth networks through the lens of diffeomorphic flows. From this perspective, the Riemannian metric with coordinate representations learned by the continuous-depth network itself is the closed-form solution of diffeomorphic flows. With the presence of ordinary differential equations, the continuous-depth network governs the velocity field to drive the different kinds of data apart from each other, which is a novel observation between the velocity field and data separation. Toy experiments confirm the proposed theory, as well as provide intuitions and visualizations as to how the velocity field governed by continuous-depth networks evolves on the manifold to operate on data. © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

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3-D Near-Field Localization by Jointly Exploiting Spatial and Temporal Information Based on a Nonuniform Cross Array

3-D Near-Field Localization by Jointly Exploiting Spatial an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zelong Yi Hua Chen Zhiwei Jiang Wei Liu Qing Wang Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Electronic Informatin Engineering Tianjin University Tianjin China

In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, the fourth-order cumulant of the near-field observations with multiple delay lags is exploited to construct virtual far-field pseudo-observations, leading to increased degrees of freedom (DOF); then, 2-D angles of the nearfield sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the one-dimensional (1-D) MUSIC algorithm is applied by resorting to the conjugate symmetry property of the signal's autocorrelation function. Numerical results are provided to demonstrate the superiority of our method.

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