Point Cloud Completion of Space Targets Based on Generative Adversarial Mapping

Generating 3D point clouds from inverse synthetic aperture radar (ISAR) image sequences is crucial for assessing the operational condition of space targets. However, the imaging characteristics of ISAR often produce sparse or incomplete point clouds. In order to surmount this constraint, we propose an innovative approach for point cloud completion leveraging the inverse transformation abilities of Generative Adversarial Networks (GANs). Our approach involves pre-training the GAN on a diverse dataset of 3D point cloud models, allowing it to learn rich geometric and semantic distributions. The proposed PointGAN module enhances feature representation by leveraging ancestral node information, thereby improving the model's adaptability and completion accuracy. Furthermore, the method operates in an unsupervised manner, generating 3D point clouds through random latent encoding and eliminating dependence on manually labeled data. Experimental evaluations indicate that the proposed method outstrips current networks with respect to chamfer distance and intersection over union (IoU) metrics. Producing 3D point clouds from sequences of inverse synthetic aperture radar (ISAR) images plays a vital role in evaluating the operational conditions of space targets. Nevertheless, the nature of ISAR imaging frequently results in point clouds that are either sparse or fragmented. To overcome this limitation, we introduce an innovative approach for completing point clouds, leveraging the inverse mapping potential inherent in Generative Adversarial Networks (GANs).