We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues. Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization imaging with deep learning to achieve high quality estimate of 3D object shape (surface normals and depth) and SVBRDF using single-view polarization imaging under frontal flash illumination. In addition to acquired polarization images, we provide our deep network with strong novel cues related to shape and reflectance, in the form of a normalized Stokes map and an estimate of diffuse color. We additionally describe modifications to network architecture and training loss which provide further qualitative improvements. We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.

中文翻译：

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用于3D形状和SVBRDF采集的深偏振成像

我们提出了一种新的方法，可以有效利用极化线索有效捕获3D对象的形状和空间变化的反射率。与以前的工作在某些约束条件下（已知形状或多视图采集）利用偏振来估计材料或对象的外观不同，我们通过将偏振成像与深度学习相结合来实现对3D对象形状（表面法线和深度）的高质量估计，从而消除了此类限制SVBRDF在正面闪光灯照明下使用单视场偏振成像。除了获取的偏振图像外，我们还为我们的深层网络提供了与形状和反射率有关的强大新颖线索，并以归一化的斯托克斯图和扩散色估计为形式。我们还将描述对网络体系结构的修改和培训损失，从而提供进一步的质量改进。与采用深度学习和闪光灯照明的最新作品相比，我们证明了我们的方法可实现优异的结果。