用生成模型解决水下4D重建中的光照失真与动态干扰问题
Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion
- 基于介质感知的扩散框架，显式建模水下散射与吸收
- 跨帧几何一致性强，对漂浮颗粒和动态干扰鲁棒
- 适合水下三维场景重建、海洋观测等应用
水下4D重建因参与介质中退化光传输与动态水流的耦合而极具挑战。现有方法多基于空气中假设，未显式处理水下吸收与后向散射，且依赖近静态假设，对漂浮颗粒和动态干扰敏感，导致几何不稳定与视图间不一致。为此，我们提出名为Ocean4D的生成式水下4D重建框架，包含两个互补组件：4D-GCC构建具有更好跨帧覆盖的4D几何一致性条件；介质感知模块在潜在扩散过程中实现隐式介质感知去噪，稳定吸收与散射下的水下外观。给定单目视频与目标相机轨迹，本方法可生成沿目标路径的视频，同时保持全局结构与跨视角一致性。在动态与静态水下基准上的大量实验表明，该方法达到当前最优性能。
原文摘要 · Abstract (English)
Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variations. Most existing Methods are developed under in-air assumptions and do not explicitly account for underwater absorption and backscatter. Additionally, near-static assumptions make these approaches sensitive to drifting particles and dynamic distractors , leading to unstable geometry and inconsistent cross-view results. To address these issues, we propose a generative framework for underwater 4D reconstruction, named Ocean4D, which is built on two complementary components. Specifically, 4D-GCC constructs 4D geometrically consistent conditioning with improved cross-frame coverage, while the Medium-Aware Block performs implicit medium-aware denoising in the latent diffusion process to stabilize underwater appearance under absorption and scattering. Given a monocular video and target cameras, our method generates videos along the target trajectories while preserving global structure and cross-view consistency. Extensive experiments on both dynamic and static underwater benchmarks demonstrate state-of-the-art performance on underwater reconstruction.
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