# AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal

arXiv: https://arxiv.org/abs/2502.16351 · HTML: https://mlsanity.com/paper/2502.16351/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Hallucinated Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2111.15246/) (2021)
- [RobustNeRF: Ignoring Distractors with Robust Losses](https://mlsanity.com/paper/2302.00833/) (2023)
- [SeaThru-NeRF: Neural Radiance Fields in Scattering Media](https://mlsanity.com/paper/2304.07743/) (2023)
- [WaterNeRF: Neural Radiance Fields for Underwater Scenes](https://mlsanity.com/paper/2209.13091/) (2022)
- [Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training](https://mlsanity.com/paper/2305.02756/) (2023)
- [UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction](https://mlsanity.com/paper/2410.01517/) (2024)
- [L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion](https://mlsanity.com/paper/2005.13736/) (2020)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)
- [Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey](https://mlsanity.com/paper/2308.15733/) (2023)
- [BirdNeRF: Fast Neural Reconstruction of Large-Scale Scenes From Aerial Imagery](https://mlsanity.com/paper/2402.04554/) (2024)
- [FlyNeRF: NeRF-Based Aerial Mapping for High-Quality 3D Scene Reconstruction](https://mlsanity.com/paper/2404.12970/) (2024)

## Built on by (most cited first, 8 of 8)

- [RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction](https://mlsanity.com/paper/2512.23437/) (2025)
- [3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models](https://mlsanity.com/paper/2604.05687/) (2026)
- [Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement](https://mlsanity.com/paper/2503.11175/) (2025)
- [RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction](https://mlsanity.com/paper/2505.15737/) (2025)
- [From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenes](https://mlsanity.com/paper/2509.17789/) (2025)
- [BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments](https://mlsanity.com/paper/2604.23655/) (2026)
- [Semantic-guided Gaussian Splatting for High-Fidelity Underwater Scene Reconstruction](https://mlsanity.com/paper/2509.00800/) (2025)
- [Gaussian Splatting Underwater: A Controlled Cross-Regime Study](https://mlsanity.com/paper/2608.25483/) (2026)

## Abstract (arXiv)

Neural radiance field (NeRF) research has made significant progress in modeling static video content captured in the wild. However, current models and rendering processes rarely consider scenes captured underwater, which are useful for studying and filming ocean life. They fail to address visual artifacts unique to underwater scenes, such as moving fish and suspended particles. This paper introduces a novel NeRF renderer and optimization scheme for an implicit MLP-based NeRF model. Our renderer reduces the influence of floaters and moving objects that interfere with static objects of interest by estimating a single surface per ray. We use a Gaussian weight function with a small offset to ensure that the transmittance of the surrounding media remains constant. Additionally, we enhance our model with a depth-based scaling function to upscale gradients for near-camera volumes. Overall, our method outperforms the baseline Nerfacto by approximately 7.5% and SeaThru-NeRF by 6.2% in terms of PSNR. Subjective evaluation also shows a significant reduction of artifacts while preserving details of static targets and background compared to the state of the arts.