Learning-Based Inverse Bi-Scale Material Fitting from Tabular BRDFs
Learning-Based Inverse Bi-Scale Material Fitting from Tabular BRDFs
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基于表格 BRDF 的基于学习的逆双尺度材料拟合
DOI：
10.1109/tvcg.2020.3026021
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发表时间：
2021
影响因子：
5.2
通讯作者：
Rushmeier, Holly
中科院分区：
文献类型：
--
作者：
Shi, Weiqi;Dorsey, Julie;Rushmeier, Holly
将小尺度结构与大尺度外观联系起来是材料外观设计中的一个关键要素。双尺度材料设计需要找到小尺度结构-中尺度几何形状和微尺度BRDF-产生所需的大尺度外观，表示为宏观尺度BRDF。调整小尺度几何形状和反射率以实现期望的外观可能成为繁琐的试错过程。我们提出了一个基于学习的解决方案，以适应一个目标的宏观尺度的BRDF与一个中尺度的几何形状和微观尺度的BRDF的组合。我们在这两个级别的代表性方面都面临挑战。在大尺度上，我们需要既紧凑又富有表现力的宏观尺度BRDF。在小尺度上，我们需要不同的几何图案和潜在的空间变化的微BRDF的组合。对于大规模的宏BRDF，我们提出了一个新的二维子集的表格BRDF表示，很好地保留了重要的外观特征的学习。对于小尺度的细节，我们表示几何形状和BRDF在不同的类别与不同的物理参数，以定义多个独立的连续搜索空间。为了建立大尺度宏BRDF和小尺度细节之间的映射，我们提出了一个端到端模型，该模型将子集BRDF作为输入，并对小尺度细节进行分类和参数估计，以找到准确的重建。与其他拟合方法相比，我们基于学习的解决方案提供了更高的重建精度，并覆盖了更广泛的外观。
Relating small-scale structures to large-scale appearance is a key element in material appearance design. Bi-scale material design requires finding small-scale structures – meso-scale geometry and micro-scale BRDFs – that produce a desired large-scale appearance expressed as a macro-scale BRDF. The adjustment of small-scale geometry and reflectances to achieve a desired appearance can become a tedious trial-and-error process. We present a learning-based solution to fit a target macro-scale BRDF with a combination of a meso-scale geometry and micro-scale BRDF. We confront challenges in representation at both scales. At the large scale we need macro-scale BRDFs that are both compact and expressive. At the small scale we need diverse combinations of geometric patterns and potentially spatially varying micro-BRDFs. For large-scale macro-BRDFs, we propose a novel 2D subset of a tabular BRDF representation that well preserves important appearance features for learning. For small-scale details, we represent geometries and BRDFs in different categories with different physical parameters to define multiple independent continuous search spaces. To build the mapping between large-scale macro-BRDFs and small-scale details, we propose an end-to-end model that takes the subset BRDF as input and performs classification and parameter estimation on small-scale details to find an accurate reconstruction. Compared with other fitting methods, our learning-based solution provides higher reconstruction accuracy and covers a wider gamut of appearance.