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Compared to 2D inverse synthetic aperture radar (ISAR) images of a space target, its 3D model can provide adequate details and accurate measurement parameters. However, it is challenging to tackle the problem of feature extraction and correlation during 3D reconstruction of space targets purely based on radar image sequences, due to their lack of clear evidence in imaging similarity compared to optical images. To address this problem, this paper proposes radar neural radiance fields (i.e. RaNeRF), which is a novel 3D reconstruction method using only observed ISAR image sequences. Firstly, the 3D structure of a target is represented as a continuous 6D function of space positions and viewing directions using a fully-connected deep network. Secondly, the relationship between the 3D structure and 2D ISAR images of the target is constructed to enable differential rendering of ISAR images. Our overall pipeline can thus be trained using the discrepancy between the modulus of rendered and observed ISAR images in a purely self-supervised manner without 3D supervision. Finally, the 3D mesh model of the target can be retrieved from the learned density field via marching cube. As a result, the proposed RaNeRF can directly reconstruct the 3D structure of targets without explicit feature extraction and correlation of ISAR image sequences. Both quantitative and qualitative results verify the effectiveness of the proposed method. Compared to conventional baseline methods using point clouds, our reconstructed structure is more complete and accurate. In addition, the optimized model can synthesize ISAR images at novel observation direction, which can be used for downstream tasks including data augmentation and target recognition.
Liu et al. (Sun,) studied this question.