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Neural Underwater Scene Representation

Yunkai Tang · Chengxuan Zhu · Renjie Wan · Chao Xu · Boxin Shi

Arch 4A-E Poster #207
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

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