3D-USE: From Image-Level to Scene-Level Underwater Enhancement
Jieyu Yuan, Yuanlin Zhang, Jihong Li, Chunle Guo, Huimin Lu, Chongyi Li
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.
Create a lesson
Related papers
PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image
Sneha Paul, Guile Wu, Bingbing Liu et al.
NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting
Yihan Zang, Da Li, Dominik Engel et al.
Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection
Girish A. Koushik, Diptesh Kanojia, Helen Treharne
Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration
Zhida Qu, Shengchao Chen
Using OCR Heads to Verbalize Image Semantics
Sheridan Feucht, Benno Krojer, Sarah Wang et al.
DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
Mengze Xu, Zhu Liu, Weidong Sheng et al.