NeMF: Neural Microphysics Fields.

Inbal Kom Betzer, Roi Ronen, Vadim Holodovsky, Yoav Y Schechner, Ilan Koren
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Abstract

Inverse problems in scientific imaging often seek physical characterization of heterogeneous scene materials. The scene is thus represented by physical quantities, such as the density and sizes of particles (microphysics) across a domain. Moreover, the forward image formation model is physical. An important case is that of clouds, where microphysics in three dimensions (3D) dictate the cloud dynamics, lifetime and albedo, with implications to Earth's energy balance, sustainable energy and rainfall. Current methods, however, recover very degenerate representations of microphysics. To enable 3D volumetric recovery of all the required microphysical parameters, we introduce the neural microphysics field (NeMF). It is based on a deep neural network, whose input is multi-view polarization images. NeMF is pre-trained through supervised learning. Training relies on polarized radiative transfer, and noise modeling in polarization-sensitive sensors. The results offer unprecedented recovery, including droplet effective variance. We test NeMF in rigorous simulations and demonstrate it using real-world polarization-image data.

NeMF:神经微物理场。
科学成像中的逆问题通常需要对异质场景材料进行物理特征描述。因此,场景是由物理量表示的,如整个领域中颗粒的密度和大小(微物理学)。此外,前向图像形成模型也是物理模型。一个重要的例子是云,三维(3D)微物理决定了云的动态、寿命和反照率,对地球的能量平衡、可持续能源和降雨量都有影响。然而,目前的方法只能恢复非常退化的微观物理现象。为了能够以三维体积复原所有必要的微物理参数,我们引入了神经微物理场(NeMF)。它基于深度神经网络,其输入是多视角偏振图像。NeMF 通过监督学习进行预训练。训练依赖于偏振辐射传递和偏振敏感传感器的噪声建模。结果提供了前所未有的恢复能力,包括液滴有效方差。我们对 NeMF 进行了严格的模拟测试,并使用真实世界的偏振图像数据进行了演示。
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