Accurate BRDF acquisition is important for realistic rendering, but dense gonioreflectometer measurements are slow and expensive. We study how to select a small number of BRDF measurements that are most useful for reconstructing material appearance under a learned reflectance prior. Our method combines a set encoder for sparse coordinate-value observations, a pretrained hypernetwork-based BRDF reconstructor, and a differentiable renderer. During sampler training, the reconstructor is kept fixed and gradients from BRDF-space and rendered-image losses are used to optimize measurement locations. This separates sample selection from prior fitting and encourages the sampler to choose directions that are informative under the learned material distribution. Experiments on the MERL dataset show that the proposed sampler improves low-budget reconstruction quality at 8 and 16 measurements compared with neural reconstruction baselines, while PCA-based methods remain strong at larger budgets. We further analyze the effect of image-space supervision, co-optimization, and image-only latent fitting for unseen materials.

翻译：精确的BRDF获取对于真实感渲染至关重要，但密集的测角光度仪测量过程缓慢且成本高昂。我们研究了如何利用学习到的反射先验，选择少量对重建材质外观最有用的BRDF测量样本。该方法结合了用于稀疏坐标-观测值对的集合编码器、基于预训练超网络的BRDF重建器以及可微分渲染器。在采样器训练过程中，重建器保持固定，并使用源自BRDF空间和渲染图像损失的梯度来优化测量位置。这实现了样本选择与先验拟合的分离，并鼓励采样器选择在已学习的材质分布下具有信息量的方向。在MERL数据集上的实验表明，与基于神经重建的基线方法相比，所提出的采样器在8个和16个测量样本的低预算条件下提升了重建质量，而基于PCA的方法在更大预算下仍保持优势。我们进一步分析了图像空间监督、协同优化以及仅通过图像隐空间拟合处理未见材质的影响。