ISAR-NeRF: Neural Radiance Fields for 3D Imaging of Space Target from Multi-view ISAR Images

With the rapid development of aerospace technology, especially the rise of the commercial aerospace industry, the number of space targets in orbit has increased rapidly. As aerospace activities are unprecedentedly active, developing space situation awareness technology has become an urgent need for space security. High-resolution imaging of space targets to obtain space target information is critical in space situational awareness. Compared to traditional 2D imaging, 3D imaging can provide complete information about a space target, which makes it a key technology for space situational awareness. However, due to the limitations of observation setups and imaging algorithms, all current 3D imaging methods tend to construct sparse 3D geometries and cannot infer 2D novel view images with geometry consistency. In this paper, we will focus on far-field imaging and propose ISAR-NeRF to address these problems, which encodes the 3D physical geometry of the target into an implicit neural network. According to the physical mechanisms of ISAR imaging, we can effectively determine sampling points from ISAR images and feed these sampling points into an implicit neural network to output densities and backscattering coefficients. We propose a novel differentiable radar rendering algorithm that exploits densities and backscattering coefficients to render multi-view consistent images and construct 3D geometries without any explicit association algorithms. The comparative experiments qualitatively and quantitatively show that ISAR-NeRF outperforms any other methods in both 3D reconstruction and novel view synthesis. In addition, the experiments of low SNR and noisy projection vectors demonstrate the robustness of our methods.