We investigate a multiview shape reconstruction problem based on an active surface model whose geometric evolution is driven by radar measurements acquired at sparse locations. Building on our previous work in the context of variational methods for the reconstruction of a scene conceptualized as the graph of a function, we generalize this inversion approach for a general geometry, now described by an active surface, strongly motivated by prior variational computer vision approaches to multiview stereo reconstruction from camera images. While conceptually similar, use of radar echoes within a variational scheme to drive the active surface evolution requires significant changes in regularization strategies compared to prior image based methodologies for the active surface evolution to work effectively. We describe all of these aspects and how we addressed them. While our long term objective is to develop a framework capable of fusing radar as well as other image based information, in which the active surface becomes an explicit shared reference for data fusion. In this paper, we explore the reconstruction using radar as a single modality, demonstrating that the presented approach can provide reconstructions of quality comparable to those from image based methods showing great potential for further development toward data fusion.

中文翻译：

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使用活动表面和水平集方法进行基于雷达的形状和反射率重建

我们研究了一个基于活动表面模型的多视图形状重建问题，该模型的几何演化是由在稀疏位置获取的雷达测量驱动的。基于我们之前在用于重建概念化为函数图的场景的变分方法背景下的工作，我们将这种反演方法概括为一般几何形状，现在由活动表面描述，受到先前变分计算机视觉方法的强烈推动从相机图像进行多视图立体重建。虽然在概念上相似，但与先前基于图像的方法相比，在变分方案中使用雷达回波来驱动主动表面演化需要对正则化策略进行重大更改才能使主动表面演化有效工作。我们描述了所有这些方面以及我们如何解决这些问题。虽然我们的长期目标是开发一个能够融合雷达和其他基于图像的信息的框架，其中活动表面成为数据融合的明确共享参考。在本文中，我们探索了使用雷达作为单一模态的重建，证明所提出的方法可以提供与基于图像的方法相当的重建质量，显示出进一步发展数据融合的巨大潜力。