论文标题
学习学习和采样BRDFS
Learning to Learn and Sample BRDFs
论文作者
论文摘要
我们提出了一种加速物理获取和学习神经双向反射分布函数（BRDF）模型的联合过程的方法。虽然单独的BRDF学习可以通过元学习加速，但在依赖机械过程的情况下，采集仍然很慢。我们表明，可以扩展元学习以优化物理抽样模式。在我们的方法进行了一组完全采样的BRDF进行了元训练之后，它可以快速训练新的BRDF，最多五个数量级的物理获取样品以相似的质量降低了五个数量级。我们的方法还扩展到其他线性和非线性BRDF模型，我们在广泛的评估中显示。
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.
