Problem
Research questions and friction points this paper is trying to address.
Underwater 3D Reconstruction
Visibility Loss
Color Shifts
Scene-level Enhancement
Multi-view Observations
3D-USE: From Image-Level to Scene-Level Underwater Enhancement
🤖 AI Summary
本文提出3D-USE框架，通过两阶段方法从降质的多视角水下图像中学习可见度增强的3D场景表示，解决水下3D重建中的颜色偏移和可见度损失问题。
📝 Abstract
Underwater 3D reconstruction faithfully reproduces the color shifts and visibility loss of captured views, while physical inversion may leave estimation errors in the recovered scene appearance. We formulate Underwater Scene-level Enhancement (USE) as learning a persistent, visibility-enhanced 3D scene representation from degraded multi-view underwater observations, enabling consistent enhanced rendering. Realizing USE requires both a reliable scene representation for enhancement and a consistent enhancement target without paired enhanced 3D data. Therefore, we present 3D-USE, a two-stage framework. First, the Medium Radial Basis Anchor Representation (MediumRBF) establishes a medium-aware Gaussian scene by representing water effects with shared radial-basis anchors and explicitly decomposing object and medium contributions. Based on this fixed scene representation, Appearance Transition Consensus (ATC) transfers paired 2D underwater image enhancement (UIE) knowledge into scene-global and Gaussian-local targets, avoiding direct supervision from inconsistent enhanced views. An Underwater Bilateral Appearance Field (U-BAF) then realizes these targets in Gaussian radiance and medium appearance. The scene directly renders enhanced novel views without a 2D UIE model at inference. Experiments on real underwater scenes show improved visibility and cross-view consistency while preserving reconstruction quality.
Innovation
Methods, ideas, or system contributions that make the work stand out.
3D-USE
MediumRBF
ATC
U-BAF
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