# SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

arXiv: https://arxiv.org/abs/2307.05087 · HTML: https://mlsanity.com/paper/2307.05087/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes](https://mlsanity.com/paper/2303.09431/) (2023)
- [Neural-Sim: Learning to Generate Training Data with NeRF](https://mlsanity.com/paper/2207.11368/) (2022)
- [PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier](https://mlsanity.com/paper/2103.15469/) (2021)
- [Sparse Signal Models for Data Augmentation in Deep Learning ATR](https://mlsanity.com/paper/2012.09284/) (2020)
- [Hardware-Accelerated SAR Simulation with NVIDIA-RTX Technology](https://mlsanity.com/paper/2005.09736/) (2020)

## Built on by (most cited first, 10 of 10)

- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds](https://mlsanity.com/paper/2504.00859/) (2025)
- [X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images](https://mlsanity.com/paper/2407.19436/) (2024)
- [Multi-view 3D surface reconstruction from SAR images by inverse rendering](https://mlsanity.com/paper/2502.10492/) (2025)
- [Controllable Radar Simulation with Waveform Parameter Embedding](https://mlsanity.com/paper/2506.03134/) (2025)
- [RadarGen: Automotive Radar Point Cloud Generation from Cameras](https://mlsanity.com/paper/2512.17897/) (2025)
- [mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis](https://mlsanity.com/paper/2608.28913/) (2026)
- [MarsLGPR: Mars Rover Localization with Ground Penetrating Radar](https://mlsanity.com/paper/2503.04944/) (2025)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)

## Abstract (arXiv)

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6% can be achieved by using only 12 images per class.