RNSplat: Radar Neural Splatting for 3D Reconstruction and Novel View Synthesis From ISAR Image Sequences

Abstract

With the rapid growth of aerospace activities, space situational awareness (SSA) has become increasingly important for space security. Compared with conventional two-dimensional (2D) inverse synthetic aperture radar (ISAR) image sequences, three-dimensional (3D) representations provide richer structural information. In addition, novel view synthesis (NVS) supports continuous visual interpretation and helps compensate for observation gaps. However, the limited angular coverage of single-pass observations makes stable 3D reconstruction and high-quality NVS difficult without accurate geometric calibration. To address these challenges, Radar Neural Splatting (RNSplat) is proposed for 3D reconstruction and NVS from ISAR image sequences. Specifically, a Pose Head Adaptation via Reprojection (PHARE) module is introduced to refine viewpoint parameters under a cross-view reprojection consistency constraint, thereby improving the estimation of a 3D point map. Together with the associated geometric attributes, the estimated point map is then used to construct the 3D Gaussian splatting (3DGS) representation. To better reflect ISAR image formation characteristics, a Rendering Stabilization Unit (RSU) is further introduced, using a Gaussian-sinc kernel as a PSF-based scattering response approximation to improve cross-view synthesis quality. Experimental results demonstrate that the proposed framework improves NVS performance and enhances visible structural consistency. Ablation studies further show that PHARE improves geometric consistency and 3D point map quality, while RSU enhances the consistency and quality of synthesized views.