A Differentiable Framework for Image-Based SAR Target Reconstruction with 3D Gaussian Splatting

Image-based synthetic aperture radar (SAR) target three-dimensional (3D) reconstruction is an important application for extracting target information from high-resolution two-dimensional (2D) SAR images. However, due to the complex mechanism of SAR imaging, developing a data-driven, fully automated 3D reconstruction framework with explicit physical constraints is very difficult. Thus, this paper proposes a differentiable framework for image-based SAR target reconstruction with 3D Gaussian Splatting, which can automatically realize the 3D target reconstruction from multi-view SAR images. Specifically, the proposed differentiable framework accumulates and generates simulated SAR images according to the SAR imaging geometry and projection during the forward simulation stage. By calculating the loss between the simulated and ground truth SAR images and backpropagating, the spatial positions of the 3D Gaussians are continuously adjusted until convergence, generating the 3D point cloud model of the target. The experiment is conducted on the constructed T72 SAR image dataset and the reconstruction results demonstrate the effectiveness of the proposed framework.