# Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction

arXiv: https://arxiv.org/abs/2007.10407 · HTML: https://mlsanity.com/paper/2007.10407/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Neural Implicit Surface Reconstruction using Imaging Sonar](https://mlsanity.com/paper/2209.08221/) (2022)
- [SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting](https://mlsanity.com/paper/2504.00159/) (2025)
- [Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo](https://mlsanity.com/paper/2208.00233/) (2022)
- [UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images](https://mlsanity.com/paper/2410.08092/) (2024)
- [SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping](https://mlsanity.com/paper/2511.00392/) (2025)
- [Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar](https://mlsanity.com/paper/2307.16160/) (2023)

## Abstract (arXiv)

We propose a novel approach to handling the ambiguity in elevation angle associated with the observations of a forward looking multi-beam imaging sonar, and the challenges it poses for performing an accurate 3D reconstruction. We utilize a pair of sonars with orthogonal axes of uncertainty to independently observe the same points in the environment from two different perspectives, and associate these observations. Using these concurrent observations, we can create a dense, fully defined point cloud at every time-step to aid in reconstructing the 3D geometry of underwater scenes. We will evaluate our method in the context of the current state of the art, for which strong assumptions on object geometry limit applicability to generalized 3D scenes. We will discuss results from laboratory tests that quantitatively benchmark our algorithm's reconstruction capabilities, and results from a real-world, tidal river basin which qualitatively demonstrate our ability to reconstruct a cluttered field of underwater objects.