# Acoustic Neural 3D Reconstruction Under Pose Drift

arXiv: https://arxiv.org/abs/2503.08930 · HTML: https://mlsanity.com/paper/2503.08930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior](https://mlsanity.com/paper/2212.07388/) (2022)
- [GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation](https://mlsanity.com/paper/2204.05735/) (2022)
- [CamP: Camera Preconditioning for Neural Radiance Fields](https://mlsanity.com/paper/2308.10902/) (2023)
- [Neural Implicit Surface Reconstruction using Imaging Sonar](https://mlsanity.com/paper/2209.08221/) (2022)
- [Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion](https://mlsanity.com/paper/2404.04687/) (2024)
- [Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar](https://mlsanity.com/paper/2306.09909/) (2023)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [Bathymetric Surveying with Imaging Sonar Using Neural Volume Rendering](https://mlsanity.com/paper/2404.14819/) (2024)
- [Spatial Acoustic Projection for 3D Imaging Sonar Reconstruction](https://mlsanity.com/paper/2206.02840/) (2022)
- [Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping](https://mlsanity.com/paper/2209.11757/) (2022)
- [Learning Covariances for Estimation with Constrained Bilevel Optimization](https://mlsanity.com/paper/2309.09718/) (2023)
- [NeuRSS: Enhancing AUV Localization and Bathymetric Mapping with Neural Rendering for Sidescan SLAM](https://mlsanity.com/paper/2405.05807/) (2024)
- [Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation](https://mlsanity.com/paper/2309.02525/) (2023)

## Built on by (most cited first, 3 of 3)

- [NAS-GS: Noise-Aware Sonar Gaussian Splatting](https://mlsanity.com/paper/2601.06285/) (2026)
- [TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches](https://mlsanity.com/paper/2604.08945/) (2026)
- [Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction](https://mlsanity.com/paper/2609.06261/) (2026)

## Abstract (arXiv)

We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation; small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we propose an algorithm that jointly optimizes the neural scene representation and sonar poses. Our algorithm does so by parameterizing the 6DoF poses as learnable parameters and backpropagating gradients through the neural renderer and implicit representation. We validated our algorithm on both real and simulated datasets. It produces high-fidelity 3D reconstructions even under significant pose drift.