# Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

arXiv: https://arxiv.org/abs/2306.09909 · HTML: https://mlsanity.com/paper/2306.09909/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 129 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Towards Understanding the Spectral Bias of Deep Learning](https://mlsanity.com/paper/1912.01198/) (2019)
- [T"oRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis](https://mlsanity.com/paper/2109.15271/) (2021)
- [Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields](https://mlsanity.com/paper/2104.11745/) (2021)
- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Non-line-of-Sight Imaging via Neural Transient Fields](https://mlsanity.com/paper/2101.00373/) (2021)
- [CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems](https://mlsanity.com/paper/2102.05181/) (2021)
- [Vid2Player: Controllable Video Sprites that Behave and Appear like Professional Tennis Players](https://mlsanity.com/paper/2008.04524/) (2020)
- [Fast Back-Projection for Non-Line of Sight Reconstruction](https://mlsanity.com/paper/1703.02016/) (2017)
- [Neural Implicit Surface Reconstruction using Imaging Sonar](https://mlsanity.com/paper/2209.08221/) (2022)
- [Non-Line-of-Sight Reconstruction using Efficient Transient Rendering](https://mlsanity.com/paper/1809.08044/) (2018)
- [Steady-state Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1811.09910/) (2018)
- [Differentiable Transient Rendering](https://mlsanity.com/paper/2206.06193/) (2022)
- [Implicit Neural Representations for Deconvolving SAS Images](https://mlsanity.com/paper/2112.08539/) (2021)
- [Sound Synthesis, Propagation, and Rendering: A Survey](https://mlsanity.com/paper/2011.05538/) (2020)
- [Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification](https://mlsanity.com/paper/1905.11595/) (2019)
- [GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction](https://mlsanity.com/paper/2101.05888/) (2021)
- [SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations](https://mlsanity.com/paper/2204.10428/) (2022)
- [Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging](https://mlsanity.com/paper/1805.03269/) (2018)
- [Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation](https://mlsanity.com/paper/1909.06436/) (2019)
- [Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus](https://mlsanity.com/paper/2103.10312/) (2021)
- [Approximate Extraction of Late-Time Returns via Morphological Component Analysis](https://mlsanity.com/paper/2208.06056/) (2022)
- [Deep Autofocus for Synthetic Aperture Sonar](https://mlsanity.com/paper/2010.15687/) (2020)

## Built on by (most cited first, 16 of 16)

- [DART: Implicit Doppler Tomography for Radar Novel View Synthesis](https://mlsanity.com/paper/2403.03896/) (2024)
- [Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar](https://mlsanity.com/paper/2405.04662/) (2024)
- [Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion](https://mlsanity.com/paper/2404.04687/) (2024)
- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [SpINR: Neural Volumetric Reconstruction for FMCW Radars](https://mlsanity.com/paper/2503.23313/) (2025)
- [Multi-view 3D surface reconstruction from SAR images by inverse rendering](https://mlsanity.com/paper/2502.10492/) (2025)
- [Acoustic Neural 3D Reconstruction Under Pose Drift](https://mlsanity.com/paper/2503.08930/) (2025)
- [NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity](https://mlsanity.com/paper/2406.10212/) (2024)
- [RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields](https://mlsanity.com/paper/2605.26328/) (2026)
- [NeRF-enabled Analysis-Through-Synthesis for ISAR Imaging of Small Everyday Objects with Sparse and Noisy UWB Radar Data](https://mlsanity.com/paper/2410.10085/) (2024)
- [SpINRv2: Implicit Neural Representation for Passband FMCW Radars](https://mlsanity.com/paper/2506.08163/) (2025)
- [SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar](https://mlsanity.com/paper/2509.11087/) (2025)
- [Single View Seafloor Recovery from Imaging Sonar via Differentiable Rendering](https://mlsanity.com/paper/2605.24195/) (2026)
- [Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction](https://mlsanity.com/paper/2609.06261/) (2026)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)

## Abstract (arXiv)

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.