# SURGE: Sonar-fUsed Reconstruction and localization via image-gated Graph Estimation

arXiv: https://arxiv.org/abs/2610.07472 · HTML: https://mlsanity.com/paper/2610.07472/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Underwater scene reconstruction, one of 15 directions in 3D reconstruction and neural rendering.
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- 3D reconstruction and neural rendering / [Underwater scene reconstruction](https://mlsanity.com/recon3d/underwater-scene-reconstruction/)

## Builds on (most cited first, 15 of 15)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [WaterSplatting: Fast Underwater 3D Scene Reconstruction Using Gaussian Splatting](https://mlsanity.com/paper/2408.08206/) (2024)
- [SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model](https://mlsanity.com/paper/2409.17345/) (2024)
- [Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion](https://mlsanity.com/paper/2404.04687/) (2024)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting](https://mlsanity.com/paper/2504.00159/) (2025)
- [Acoustic Neural 3D Reconstruction Under Pose Drift](https://mlsanity.com/paper/2503.08930/) (2025)
- [Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments](https://mlsanity.com/paper/2202.08359/) (2022)
- [Opti-Acoustic Scene Reconstruction in Highly Turbid Underwater Environments](https://mlsanity.com/paper/2508.03408/) (2025)
- [DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams](https://mlsanity.com/paper/2210.00867/) (2022)
- [DRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching](https://mlsanity.com/paper/2507.23629/) (2025)
- [OASIS: Real-Time Opti-Acoustic Sensing for Intervention Systems in Unstructured Environments](https://mlsanity.com/paper/2508.12071/) (2025)
- [Calibration and Comparative Analysis of Forward-Looking Sonar and 3D Sonar for Enhanced Underwater Object Recognition](https://mlsanity.com/paper/2608.29433/) (2026)
- [Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction](https://mlsanity.com/paper/2609.06261/) (2026)

## Abstract (arXiv)

Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure. These missions require accurate 3D understanding of the surrounding environment, which depends on both reliable vehicle localization and metric scene reconstruction. However, external positioning is often unavailable underwater, requiring small ROVs to rely primarily on onboard perception. Optic vision provides rich visual and geometric information but suffers from scale ambi- guity and trajectory drift, whereas 2D imaging sonar provides metric range but incomplete 3D geometry. Existing underwater reconstruction approaches typically address these limitations separately or assume known sensor poses, leaving localization and reconstruction disconnected. We present SURGE, a camera sonar framework that jointly estimates the ROV trajectory and target location by integrating visual and acoustic observations within a factor graph, then uses the recovered metric poses for sonar Gaussian splatting. Experiments on real underwater RGB sonar observations show that SURGE substantially improves localization consistency over conventional vision based pose estimation and produces a more compact, natively metric reconstruction than RGB Gaussian splatting baselines.