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Volumetric Cloud Field Reconstruction

2023-11-29 14:19:40
Jacob Lin, Miguel Farinha, Edward Gryspeerdt, Ronald Clark


Volumetric phenomena, such as clouds and fog, present a significant challenge for 3D reconstruction systems due to their translucent nature and their complex interactions with light. Conventional techniques for reconstructing scattering volumes rely on controlled setups, limiting practical applications. This paper introduces an approach to reconstructing volumes from a few input stereo pairs. We propose a novel deep learning framework that integrates a deep stereo model with a 3D Convolutional Neural Network (3D CNN) and an advection module, capable of capturing the shape and dynamics of volumes. The stereo depths are used to carve empty space around volumes, providing the 3D CNN with a prior for coping with the lack of input views. Refining our output, the advection module leverages the temporal evolution of the medium, providing a mechanism to infer motion and improve temporal consistency. The efficacy of our system is demonstrated through its ability to estimate density and velocity fields of large-scale volumes, in this case, clouds, from a sparse set of stereo image pairs.

Abstract (translated)

体积现象(如云和雾)对 3D 重建系统来说是一个显著的挑战,由于它们的透明性质以及与光线复杂的相互作用。传统的散射体积重建方法依赖于控制设置,限制了其实用性。本文提出了一种从几个输入立体对中重构体积的方法。我们提出了一种新颖的深度学习框架,将深度立体模型与 3D 卷积神经网络(3D CNN)和加速度模块相结合,能够捕捉体积的形状和动态。立体深度被用来在体积周围挖空空间,为 3D CNN 提供了一种应对缺少输入视图的先前经验。通过优化我们的输出,加速度模块利用了中场的时变性,提供了一种机制来推断运动并改善时间一致性。我们系统的有效性通过其能够从稀疏的立体图像对中估计大规模体积的密度和速度场来证明。在这种情况下,我们的大规模云。



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