We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta-learning, acquisition remains slow as it relies on a mechanical process. We show that meta-learning can be extended to optimize the physical sampling pattern, too. After our method has been meta-trained for a set of fully-sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non-linear BRDF models, which we show in an extensive evaluation.

翻译：我们提出一种加速物理采集和神经双向反射分布函数（BRDF）模型联合过程的方法。尽管仅靠元学习可以加速BRDF学习，但采集过程因依赖机械操作而仍然缓慢。我们证明元学习也可扩展用于优化物理采样模式。在方法经过一组全采样BRDF的元训练后，它能够以最多五个数量级更少的物理采集样本，在相似质量下快速训练新BRDF。我们的方法还可推广至其他线性和非线性BRDF模型，我们在广泛评估中对此进行了展示。