arXiv subjects

Xiaoyan Huang

Publications and source records attributed to Xiaoyan Huang.

5 recordsLinked to original sources

arXiv2026

IUP-Pose: Decoupled Iterative Uncertainty Propagation for Real-time Relative Pose Regression via Implicit Dense Alignment v1

Relative pose estimation is fundamental for SLAM, visual localization, and 3D reconstruction. Existing Relative Pose Regression (RPR) methods face a key trade-off: feature-matching pipelines achieve high accuracy but block gradient flow via non-differentiable RANSAC, while ViT-based regressors are end-to-end trainable but prohibitively expensive for real-time deployment. We identify the core bottlenecks as the coupling between rotation and translation estimation and insufficient cross-view feature alignment. We propose IUP-Pose, a geometry-driven decoupled iterative framework with implicit dense alignment. A lightweight Multi-Head Bi-Cross Attention (MHBC) module aligns cross-view features without explicit matching supervision. The aligned features are processed by a decoupled rotation-translation pipeline: two shared-parameter rotation stages iteratively refine rotation with uncertainty, and feature maps are realigned via rotational homography H_inf before translation prediction. IUP-Pose achieves 73.3% AUC@20deg on MegaDepth1500 with full end-to-end differentiability, 70 FPS throughput, and only 37M parameters, demonstrating a favorable accuracy-efficiency trade-off for real-time edge deployment.

cs.CV↗

arXiv2025

EpiPlanAgent: Agentic Automated Epidemic Response Planning

Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system using large language models (LLMs) to automate the generation and validation of digital emergency response plans. The multi-agent framework integrated task decomposition, knowledge grounding, and simulation modules. Public health professionals tested the system using real-world outbreak scenarios in a controlled evaluation. Results demonstrated that EpiPlanAgent significantly improved the completeness and guideline alignment of plans while drastically reducing development time compared to manual workflows. Expert evaluation confirmed high consistency between AI-generated and human-authored content. User feedback indicated strong perceived utility. In conclusion, EpiPlanAgent provides an effective, scalable solution for intelligent epidemic response planning, demonstrating the potential of agentic AI to transform public health preparedness.

cs.AI↗

arXiv2023

Inverse design of dielectric metasurface by spatial coupled mode theory

Modeling metasurfaces with high accuracy and efficiency is challenging because they have features smaller than the wavelength but sizes much larger than the wavelength. Full wave simulation is accurate but very slow. Popular design paradigms like locally periodic approximation (LPA) reduce the computational cost by neglecting, partially or fully, near-field interactions between meta-units and treating them in an isolated manner. The coupling between meta units has been fully considered by applying temporal coupled mode theory to model the metasurface. However, this method only works for resonance-based metasurfaces. To model the broadly studied dielectric metasurfaces based on the propagation of guided modes, we propose to model the whole system using spatial coupled mode theory (SCMT) where the dielectric metasurface can be viewed as an array of truncated waveguides. An inverse design routine based on this model is then devised and applied to gain improvements over LPA in several scenarios, such as high numerical aperture lens, multi-wavelength focusing, and suppression of coma aberrations. With its accuracy and efficiency, the proposed framework can be a powerful tool to improve the performance of dielectric metasurface on various tasks.

physics.optics↗

arXiv2022

Metasurface Smart Glass for Object Recognition

Recent years have seen a considerable surge of research on developing heuristic approaches to realize analog computing using physical waves. Among these, neuromorphic computing using light waves is envisioned to feature performance metrics such as computational speed and energy efficiency exceeding those of conventional digital techniques by many orders of magnitude. Yet, neuromorphic computing based on photonics remains a challenge due to the difficulty of training and manufacturing sophisticated photonic structures to support neural networks with adequate expressive power. Here, we realize a diffractive optical neural network (ONN) based on metasurfaces that can recognize objects by directly processing light waves scattered from the objects. Metasurfaces composed of a two-dimensional array of millions of meta-units can realize precise control of optical wavefront with subwavelength resolution; thus, when used as constitutive layers of an ONN, they can provide exceptionally high expressive power. We experimentally demonstrate ONNs based on single-layered metasurfaces that modulate the phase and polarization over optical wavefront for recognizing optically coherent binary objects, including hand-written digits and English alphabetic letters. We further demonstrate, in simulation, ONNs based on metasurface doublets for human facial verification. The advantageous traits of metasurface-based ONNs, including ultra-compact form factors, zero power consumption, ultra-fast and parallel data processing capabilities, and physics-guaranteed data security, make them suitable as "edge" perception devices that can transform the future of image collection and analysis.

physics.optics↗

arXiv2022

Metasurface Holographic Optical Traps for Ultracold Atoms

We propose metasurface holograms as a novel platform to generate optical trap arrays for cold atoms with high fidelity, efficiency, and thermal stability. We developed design and fabrication methodologies to create dielectric, phase-only metasurface holograms based on titanium dioxide. We experimentally demonstrated optical trap arrays of various geometries, including periodic and aperiodic configurations with dimensions ranging from 1D to 3D and the number of trap sites up to a few hundred. We characterized the performance of the holographic metasurfaces in terms of the positioning accuracy, size and intensity uniformity of the generated traps, and power handling capability of the dielectric metasurfaces. Our proposed platform has great potential for enabling fundamental studies of quantum many-body physics, and quantum simulation and computation tasks. The compact form factor, passive nature, good power handling capability, and scalability of generating high-quality, large-scale arrays also make the metasurface platform uniquely suitable for realizing field-deployable devices and systems based on cold atoms.

physics.optics↗