# Differentiable SAR Renderer and SAR Target Reconstruction

arXiv: https://arxiv.org/abs/2205.07099 · HTML: https://mlsanity.com/paper/2205.07099/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 8 of 8)

- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation](https://mlsanity.com/paper/1908.01491/) (2019)
- [A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images](https://mlsanity.com/paper/1903.04704/) (2019)
- [A Survey on Deep Geometry Learning: From a Representation Perspective](https://mlsanity.com/paper/2002.07995/) (2020)
- [Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks](https://mlsanity.com/paper/1901.08236/) (2019)
- [Inverse Graphics with Probabilistic CAD Models](https://mlsanity.com/paper/1407.1339/) (2014)

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

- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing](https://mlsanity.com/paper/2401.01175/) (2024)
- [Reinforcement Learning for SAR View Angle Inversion with Differentiable SAR Renderer](https://mlsanity.com/paper/2401.01165/) (2024)
- [SAR2Struct: Extracting 3D Semantic Structural Representation of Aircraft Targets from Single-View SAR Image](https://mlsanity.com/paper/2506.06757/) (2025)

## Abstract (arXiv)

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt to the inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed which reformulates the mapping and projection algorithm of SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using both synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach.