Jie Chen

Plasmonic Analog of Klein Tunneling in Binary Graphene Sheet Arrays

Fan Yang, Lixia Xiao, Tao Wei, Jie Chen

We theoretically investigate the plasmonic analog of the relativistic Klein tunneling processes in systems of two coupled graphene waveguide arrays with spatially varying chemical potentials. By introducing an offset in the chemical potentials of the graphene sheets, a potential step is generated …

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Brain State Dynamics and Developmental Differences in Reading Comprehension

Jia Zhang, Lanfang Liu, Jie Chen, Ningxin Zhao et autres

Abstract Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with …

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Towards Efficient 3D Gaussian Human Avatar Compression: A Prior-Guided Framework

Shanzhi Yin, Bolin Chen, Xiaoqing Wu, Ru-Ling Liao et autres

This paper proposes an efficient 3D avatar coding framework that leverages compact human priors and canonical-to-target transformation to enable high-quality 3D human avatar video compression at ultra-low bit rates. The framework begins by training a canonical Gaussian avatar using articulated splatting in …

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Learning Context-Adaptive Motion Priors for Masked Motion Diffusion Models with Efficient Kinematic Attention Aggregation

Junkun Jiang, Jie Chen, Ho Yin Au, Jingyu Xiang

Vision-based motion capture solutions often struggle with occlusions, which result in the loss of critical joint information and hinder accurate 3D motion reconstruction. Other wearable alternatives also suffer from noisy or unstable data, often requiring extensive manual cleaning and correction to achieve …

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Multi-Scale K-Cosine Curvature Fusion With Nonlinear Measure for Robust Corner Detection

Jie Chen, Tianhang Tang, Shun Lv, Yiguang Liu

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RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories

Qingsong Yan, Qiang Wang, Kaiyong Zhao, Jie Chen et autres

Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful tools for 3D reconstruction and SLAM tasks. However, their performance depends heavily on accurate camera pose priors. Existing approaches attempt to address this issue by introducing external constraints but …

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Research on infrared image gas leakage detection method based on spatiotemporal multifeature fusion

Xiaoxiao Li, Hongjie Yang, Lingzhi Wang, Jie Chen et autres

To address the issue of false alarms potentially caused by environmental interference in infrared gas leakage detection, this paper proposes a detection method based on spatiotemporal multi-feature fusion. By jointly analyzing spatial static features (such as shape) and temporal dynamic features (such …

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Hyperspectral image compression with deep learning: A review

Fahad Saeed, Shumin Liu, Yelin Liu, Jie Chen

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Dual-Balancing for Physics-Informed Neural Networks

Chenhong Zhou, Jie Chen, Zaifeng Yang, Ching Eng Png

Physics-informed neural networks (PINNs) have emerged as a new learning paradigm for solving partial differential equations (PDEs) by enforcing the constraints of physical equations, boundary conditions (BCs), and initial conditions (ICs) into the loss function. Despite their successes, vanilla PINNs still suffer …

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UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting

Wenpeng Xing, Jie Chen, Zaifeng Yang, Changting Lin et autres

Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like Neural Radiance Fields (NeRF). While NeRF extensions such as SeaThru-NeRF incorporate physics-based models, their MLP reliance limits efficiency and …

Linear Structure Recognition Algorithm Based on UAV Color Point Cloud

Jie Chen, Xuejia Sang, Dunlong Liu

Accurate identification of linear structures is crucial for terrain feature recognition. Traditional identification methods rely on manual observation, which are time-consuming, labor-intensive, and susceptible to subjective bias. Therefore, developing an efficient, accurate, and safe method for linear structure recognition holds significant practical …

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Knowledge-driven interpretative conditional diffusion model for contrast-free myocardial infarction enhancement synthesis

Ronghui Qi, Min Tao, Chenchu Xu, Xiaohu Li et autres

Synthesis of myocardial infarction enhancement (MIE) images without contrast agents (CAs) has shown great potential to advance myocardial infarction (MI) diagnosis and treatment. It provides results comparable to late gadolinium enhancement (LGE) images, thereby reducing the risks associated with CAs and streamlining …

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