时空降质感知的3D高斯点散布用于逼真水下场景重建
Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction
AI总结：
本文提出MarineSTD-GS框架，通过建模时空降质实现逼真水下场景重建，引入内在高斯和降质高斯，结合时空降质建模模块，改进几何和外观估计，实验表明其在处理时空降质和合成新视角方面优于现有方法。
摘要：
从水下视频重建逼真水下场景仍然是多媒体领域有意义却具有挑战性的工作。水下成像固有的时空降质，包括衍射、闪烁、衰减和后向散射，经常导致现有3D重建方法的几何和外观不准确。尽管一些近期工作探索了水下降质感知重建，但它们通常只处理空间或时间降质之一，在更真实的水下场景中两者同时存在时表现不足。我们提出MarineSTD-GS，一种基于3D高斯点散布的新框架，明确建模时间和空间降质以实现逼真水下场景重建。具体来说，我们引入了两个配对的高斯原语：内在高斯代表真实场景，而降质高斯渲染降质观测。每个降质高斯的颜色通过其配对的内在高斯通过时空降质建模（SDM）模块物理推导，使自我监督地分离现实外观与降质图像。为了确保稳定训练和准确几何，我们进一步提出了深度引导的几何损失和多阶段优化策略。我们还构建了一个包含多样空间和时间降质及真实外观的模拟基准以进行全面评估。在模拟和真实数据集上的实验表明，MarineSTD-GS稳健地处理时空降质，并在新视角合成中以逼真、无水场景外观优于现有方法。
英文摘要：
Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatiotemporal degradations in underwater imaging, including caustics, flickering, attenuation, and backscattering, frequently result in inaccurate geometry and appearance in existing 3D reconstruction methods. While a few recent works have explored underwater degradation-aware reconstruction, they often address either spatial or temporal degradation alone, falling short in more real-world underwater scenarios where both types of degradation occur. We propose MarineSTD-GS, a novel 3D Gaussian Splatting-based framework that explicitly models both temporal and spatial degradations for realistic underwater scene reconstruction. Specifically, we introduce two paired Gaussian primitives: Intrinsic Gaussians represent the true scene, while Degraded Gaussians render the degraded observations. The color of each Degraded Gaussian is physically derived from its paired Intrinsic Gaussian via a Spatiotemporal Degradation Modeling (SDM) module, enabling self-supervised disentanglement of realistic appearance from degraded images. To ensure stable training and accurate geometry, we further propose a Depth-Guided Geometry Loss and a Multi-Stage Optimization strategy. We also construct a simulated benchmark with diverse spatial and temporal degradations and ground-truth appearances for comprehensive evaluation. Experiments on both simulated and real-world datasets show that MarineSTD-GS robustly handles spatiotemporal degradations and outperforms existing methods in novel view synthesis with realistic, water-free scene appearances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
