Lightweight Pixel2mesh for 3-D Target Reconstruction From a Single SAR Image

Three-dimensional target reconstruction from 2-D synthetic aperture radar (SAR) images can reduce the difficulty of data acquisition when compared to 3-D imaging technologies. Pixel2mesh was well designed for 3-D target reconstruction from 2-D optical images. However, when it is directly applied to small SAR datasets, overfitting is prone to occur. In this letter, we propose a lightweight Pixel2mesh for 3-D target reconstruction from a single 2-D SAR image. Based on the original Pixel2mesh, we improve two subnetworks: feature extraction and 3-D deformation. First, we lightweight the graph residual network (G-ResNet) module in the 3-D deformation subnetwork to avoid overfitting. Second, we add a decoder after the feature extraction subnetwork to reconstruct the input image and then obtain the image reconstruction loss to guide the training of the whole network. In addition, we modify the Laplace regularization term for the training of the proposed network, aiming to make the deformation more reasonable. Experiments are implemented on the Gotcha dataset, where 2-D images of seven cars are obtained by performing 2-D imaging. The 3-D labels of these cars are generated by using their CAD models downloaded from public websites. Experimental results verify that our network can achieve better 3-D reconstruction results than the original Pixel2mesh.