In this paper, we introduce a technique to estimate measured BRDFs from a sparse set of samples. Our approach offers accurate BRDF reconstructions that are generalizable to new materials. This opens the door to BDRF reconstructions from a variety of data sources. The success of our approach relies on the ability of hypernetworks to generate a robust representation of BRDFs and a set encoder that allows us to feed inputs of different sizes to the architecture. We evaluate our technique both qualitatively and quantitatively on the well-known MERL dataset of 100 isotropic materials. Our approach accurately estimates the BRDFs of unseen materials even for an extremely sparse sampling.

翻译：本文提出一种从稀疏采样数据中估计实测BRDF（双向反射分布函数）的技术。该方法能够实现精确的BRDF重建，并具有良好的材料通用性，为多数据源的BRDF重建开辟了新途径。技术成功的关键在于超网络能够生成BRDF的鲁棒表征，同时构建的集合编码器允许向架构输入不同尺寸的数据。我们在包含100种各向同性材料的经典MERL数据集上进行了定性与定量评估。实验表明，即便面对极端稀疏的采样数据，该方法仍能准确估计未见过材料的BRDF。