OceanSplat: 基于三目视图一致性的物体感知高斯点云法用于水下场景重建
OceanSplat: Object-aware Gaussian Splatting with Trinocular View Consistency for Underwater Scene Reconstruction
AI总结：
OceanSplat通过三目视图一致性与深度感知调整，实现高保真水下场景重建与恢复。
摘要：
我们介绍了OceanSplat，一种基于3D高斯点云的新方法，用于高保真的水下场景重建。为克服由散射介质引起的多视图不一致问题，我们通过从水平和垂直平移的虚拟视角渲染，为每个相机姿态设计三目设置，强制视图一致性以促进3D高斯点云的空间优化。此外，我们从虚拟视角推导出合成的极线深度先验，这些先验作为自监督的深度正则化器，以补偿在退化的水下场景中有限的几何线索。我们还提出了一种基于深度的alpha调整，该调整根据高斯点云沿视线方向的深度，在早期训练中调节其不透明度，以防止介质诱导的原始体的形成。我们的方法通过有效的几何约束促进了3D高斯点云与散射介质的解耦，使场景结构的准确表示成为可能，并显著减少了漂浮伪影。在真实水下和模拟场景上的实验表明，OceanSplat在散射介质中的场景重建和恢复方面显著优于现有方法。
英文摘要：
We introduce OceanSplat, a novel 3D Gaussian Splatting-based approach for high-fidelity underwater scene reconstruction. To overcome multi-view inconsistencies caused by scattering media, we design a trinocular setup for each camera pose by rendering from horizontally and vertically translated virtual viewpoints, enforcing view consistency to facilitate spatial optimization of 3D Gaussians. Furthermore, we derive synthetic epipolar depth priors from the virtual viewpoints, which serve as self-supervised depth regularizers to compensate for the limited geometric cues in degraded underwater scenes. We also propose a depth-aware alpha adjustment that modulates the opacity of 3D Gaussians during early training based on their depth along the viewing direction, deterring the formation of medium-induced primitives. Our approach promotes the disentanglement of 3D Gaussians from the scattering medium through effective geometric constraints, enabling accurate representation of scene structure and significantly reducing floating artifacts. Experiments on real-world underwater and simulated scenes demonstrate that OceanSplat substantially outperforms existing methods for both scene reconstruction and restoration in scattering media.
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