Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals or plastics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from light-field cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup. We show that, although direct shape recovery is not possible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.

中文翻译：

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SVBRDF-光场相机的不变形状和反射率估计


光场相机最近已成为一次性被动 3D 形状捕捉的强大工具。然而，获得金属或塑料等有光泽物体的形状仍然具有挑战性，因为无法轻松应用标准朗伯线索（如照片一致性）。在本文中，我们推导了一种空间变化 (SV)BRDF 不变理论，用于从光场相机恢复 3D 形状和反射率。我们的关键理论见解是在光场设置下对漫射加单瓣 SVBRDF 进行新颖的分析。我们表明，虽然直接形状恢复是不可能的，但仍然可以导出与深度和法线相关的方程。使用该方程，我们建议在解决形状模糊性之前使用多项式（二次）形状。一旦形状被估计，我们也会恢复反射率。我们提供了整个 MERL BRDF 数据集的大量合成数据，以及许多验证理论的真实示例，其中我们同时从使用 Lytro Illum 相机拍摄的单张图像中恢复形状和 BRDF。