arXiv subjects

Hongkun Cao

Publications and source records attributed to Hongkun Cao.

5 recordsLinked to original sources

arXiv2025

Improving Multi-View Reconstruction via Texture-Guided Gaussian-Mesh Joint Optimization

Reconstructing real-world objects from multi-view images is essential for applications in 3D editing, AR/VR, and digital content creation. Existing methods typically prioritize either geometric accuracy (Multi-View Stereo) or photorealistic rendering (Novel View Synthesis), often decoupling geometry and appearance optimization, which hinders downstream editing tasks. This paper advocates an unified treatment on geometry and appearance optimization for seamless Gaussian-mesh joint optimization. More specifically, we propose a novel framework that simultaneously optimizes mesh geometry (vertex positions and faces) and vertex colors via Gaussian-guided mesh differentiable rendering, leveraging photometric consistency from input images and geometric regularization from normal and depth maps. The obtained high-quality 3D reconstruction can be further exploit in down-stream editing tasks, such as relighting and shape deformation. Our code will be released in https://github.com/zhejia01/TexGuided-GS2Mesh

cs.CV↗

arXiv2025

ControlGS: Consistent Structural Compression Control for Deployment-Aware Gaussian Splatting

3D Gaussian Splatting (3DGS) is a highly deployable real-time method for novel view synthesis. In practice, it requires a universal, consistent control mechanism that adjusts the trade-off between rendering quality and model compression without scene-specific tuning, enabling automated deployment across different device performances and communication bandwidths. In this work, we present ControlGS, a control-oriented optimization framework that maps the trade-off between Gaussian count and rendering quality to a continuous, scene-agnostic, and highly responsive control axis. Extensive experiments across a wide range of scene scales and types (from small objects to large outdoor scenes) demonstrate that, by adjusting a globally unified control hyperparameter, ControlGS can flexibly generate models biased toward either structural compactness or high fidelity, regardless of the specific scene scale or complexity, while achieving markedly higher rendering quality with the same or fewer Gaussians compared to potential competing methods. Project page: https://zhang-fengdi.github.io/ControlGS/

cs.CV↗

arXiv2024

A Diffuse Light Field Imaging Model for Forward-Scattering Photon-Coded Signal Retrieval

Scattering imaging is often hindered by extremely low signal-to-noise ratios (SNRs) due to the prevalence of scattering noise. Light field imaging has been shown to be effective in suppressing noise and collect more ballistic photons as signals. However, to overcome the SNR limit in super-strong scattering environments, even with light field framework, only rare ballistic signals are insufficient. Inspired by radiative transfer theory, we propose a diffuse light field imaging model (DLIM) that leverages light field imaging to retrieve forward-scattered photons as signals to overcome the challenges of low-SNR imaging caused by super-strong scattering environments. This model aims to recover the ballistic photon signal as a source term from forward-scattered photons based on diffusion equations. The DLIM consists of two main processes: radiance modeling and diffusion light-field approximation. Radiate modeling analyzes the radiance distribution in scattering light field images using a proposed three-plane parameterization, which solves a 4-D radiate kernel describing the impulse function of scattering light field. Then, the scattering light field images synthesize a diffuse source satisfying the diffusion equation governing forward scattering photons, solved under Neumann boundary conditions in imaging space. This is the first physically-aware scattering light field imaging model, extending the conventional light field imaging framework from free space into diffuse space. The extensive experiments confirm that the DLIM can reconstruct the target objects even when scattering light field images are reduced as random noise at extremely low SNRs.

eess.IV↗

arXiv2024

Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design

Metasurfaces, capable of manipulating light at subwavelength scales, hold great potential for advancing optoelectronic applications. Generative models, particularly Generative Adversarial Networks (GANs), offer a promising approach for metasurface inverse design by efficiently navigating complex design spaces and capturing underlying data patterns. However, existing generative models struggle to achieve high electromagnetic fidelity and structural diversity. These challenges arise from the lack of explicit electromagnetic constraints during training, which hinders accurate structure-to-electromagnetic response mapping, and the absence of mechanisms to handle one-to-many mappings dilemma, resulting in insufficient structural diversity. To address these issues, we propose the Anchor-controlled Generative Adversarial Network (AcGAN), a novel framework that improves both electromagnetic fidelity and structural diversity. To achieve high electromagnetic fidelity, AcGAN proposes the Spectral Overlap Coefficient (SOC) for precise spectral fidelity assessment and develops AnchorNet, which provides real-time feedback on electromagnetic performance to refine the structure-to-electromagnetic mapping. To enhance structural diversity, AcGAN incorporates a cluster-guided controller that refines input processing and ensures multi-level spectral integration, guiding the generation process to explore multiple configurations for the same spectral target. Additionally, a dynamic loss function progressively shifts the focus from data-driven learning to optimizing both spectral fidelity and structural diversity. Empirical analysis shows that AcGAN reduces the Mean Squared Error (MSE) by 73% compared to current state-of-the-art GANs methods and significantly expands the design space to generate diverse metasurface architectures that meet precise spectral demands.

physics.optics↗

arXiv2023

Topological structures of energy flow: Poynting vector skyrmions

Topological properties of energy flow of light are fundamentally interesting and have rich practical applications in optical manipulations. Here, skyrmion-like structures formed by Poynting vectors are unveiled in the focal region of a pair of counter-propagating cylindrical vector vortex beams in free space. A Néel-Bloch-Néel skyrmion type transformation of Poynting vectors is observed along the light propagating direction within a volume with subwavelength feature sizes. The corresponding skyrmion type can be determined by the phase singularities of the individual components of the coherently superposed electromagnetic field in the focal region. This work reveals a new family member of optical skyrmions and may introduce novel physical phenomena associated with light scattering and optical force.

physics.optics↗