Synthetic aperture radar (SAR) 3D point clouds reconstruction can eliminate the problems of layover in 2D SAR image projections, the recognition of the reconstructed point cloud can significantly enhance target identification and information extraction. Current SAR 3D point cloud segmentation methods, based on traditional machine learning clustering approaches, suffer from poor automation and accuracy. However, the absence of publicly labeled SAR datasets impedes the progress of deep learning-based SAR 3D point cloud segmentation methods. To tackle the aforementioned challenges, we introduce, for the first time, an alternative training approach for SAR 3D point cloud segmentation using LiDAR annotated data, which offers a more abundant sample pool, thus alleviating the lack of training sets. Nevertheless, a segmentation model trained on LiDAR point clouds exhibits a significant decline in performance when directly applied to SAR 3D reconstructed point clouds due to the cross-domain discrepancy. This research presents a pioneering domain adaptation 3D semantic segmentation framework to implement cross-modal learning for SAR point clouds. In our scheme, a simple yet effective technique is developed to achieve segmentation of SAR double-bounce scattering regions called SARDBS-Mix, which employs a mixing strategy, to capture the distinctive reflection characteristics of SAR data. Furthermore, we implement approaches including center alignment and normalization, local augmentation, and weighted cross entropy to mitigate LiDAR and SAR domain gap and class imbalances. The experimental results validate the feasibility and effectiveness of the proposed method for SAR 3D reconstructed point cloud segmentation.

中文翻译：

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一种利用 LiDAR 数据的跨模态 SAR 三维重建点云分割的域自适应框架


合成孔径雷达 （SAR） 三维点云重建可以消除二维 SAR 图像投影中的滞留问题，对重建后的点云的识别可以显著提高目标识别和信息提取能力。目前的 SAR 三维点云分割方法基于传统的机器学习聚类方法，自动化和准确性较差。然而，缺乏公开标记的 SAR 数据集阻碍了基于深度学习的 SAR 3D 点云分割方法的发展。为了应对上述挑战，我们首次引入了一种使用 LiDAR 注释数据的 SAR 3D 点云分割的替代训练方法，该方法提供了更丰富的样本池，从而缓解了训练集的缺乏。然而，由于跨域差异，在 LiDAR 点云上训练的分割模型在直接应用于 SAR 3D 重建点云时表现出性能显着下降。本研究提出了一种开创性的域自适应 3D 语义分割框架，用于实现 SAR 点云的跨模态学习。在我们的方案中，开发了一种简单而有效的技术来实现 SAR 双反弹散射区域的分割，称为 SARDBS-Mix，它采用混合策略来捕获 SAR 数据的独特反射特性。此外，我们实施了包括中心对齐和归一化、局部增强和加权交叉熵在内的方法，以减轻 LiDAR 和 SAR 域差距和类不平衡。实验结果验证了所提方法用于 SAR 三维重建点云分割的可行性和有效性。