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. The set encoder and the hypernetwork also enable the compression of densely sampled BRDFs. We evaluate our technique both qualitatively and quantitatively on the well-known MERL dataset of 100 isotropic materials. Our approach accurately 1) estimates the BRDFs of unseen materials even for an extremely sparse sampling, 2) compresses the measured BRDFs into very small embeddings, e.g., 7D.

翻译：本文提出一种从稀疏样本中估计实测BRDF的技术。该方法能够实现精确的BRDF重建，并可泛化至新材料，从而为多种数据源的BRDF重建开辟了新途径。其成功依赖于超网络生成鲁棒BRDF表征的能力，以及允许架构接收不同尺寸输入数据的集合编码器。该集合编码器与超网络还能对密集采样BRDF进行压缩。我们在包含100种各向同性材料的著名MERL数据集上进行了定性与定量评估。实验表明，该方法能精确：1）在极端稀疏采样条件下估计未见材料的BRDF；2）将实测BRDF压缩为极小嵌入（如7维向量）。