# Spatial Acoustic Projection for 3D Imaging Sonar Reconstruction

arXiv: https://arxiv.org/abs/2206.02840 · HTML: https://mlsanity.com/paper/2206.02840/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Robust Model-Aided Inertial Localization for Autonomous Underwater Vehicles](https://mlsanity.com/paper/1805.08011/) (2018)

## Built on by (most cited first, 4 of 4)

- [Neural Implicit Surface Reconstruction using Imaging Sonar](https://mlsanity.com/paper/2209.08221/) (2022)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [Acoustic Neural 3D Reconstruction Under Pose Drift](https://mlsanity.com/paper/2503.08930/) (2025)
- [Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction](https://mlsanity.com/paper/2609.06261/) (2026)

## Abstract (arXiv)

In this work we present a novel method for reconstructing 3D surfaces using a multi-beam imaging sonar. We integrate the intensities measured by the sonar from different viewpoints for fixed cell positions in a 3D grid. For each cell we integrate a feature vector that holds the mean intensity for a discretized range of viewpoints. Based on the feature vectors and independent sparse range measurements that act as ground truth information, we train convolutional neural networks that allow us to predict the signed distance and direction to the nearest surface for each cell. The predicted signed distances can be projected into a truncated signed distance field (TSDF) along the predicted directions. Utilizing the marching cubes algorithm, a polygon mesh can be rendered from the TSDF. Our method allows a dense 3D reconstruction from a limited set of viewpoints and was evaluated on three real-world datasets.