mlsanityData to 7 Oct 2026

Differentiable SAR Renderer and SAR Target Reconstruction

arXiv 2205.07099 ↗First posted 14 May 2022cs.CV eess.IVcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 14 May 2022.

Builds on

8 of 25 references.

References · 8

  1. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  2. 2017Neural 3D Mesh Renderercited by 437
  3. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  4. 2019Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformationcited by 103
  5. 2019A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Imagescited by 37
  6. 2020A Survey on Deep Geometry Learning: From a Representation Perspectivecited by 19
  7. 2019Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networkscited by 11
  8. 2014Inverse Graphics with Probabilistic CAD Modelscited by 2

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.