We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which allows rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.

翻译：我们提出NeuMIP(NeuMIP),这是在不同尺度上代表并展示各种物质外观的一种神经方法。古典预过滤(Mipmapping)方法在扩散颜色等简单物质特性方面效果良好,但未能普及到正常、自我阴影、纤维或更复杂的微结构及反射。在这项工作中,我们将传统的 Mipmap金字塔推广到神经质质质的金字塔上,并结合一个完全连接的网络。我们还引入神经补丁,这是一种新颖的方法,可以使材料具有复杂的副作用,而不会产生任何星系。这种方法一般化了古典的parlax映射图,但未经任何明确的高度场的监督而经过培训。我们系统中的神经材料支持一个7维的查询,包括位置、进出方向和所需的过滤内核尺寸。材料的储存量很小(按标准的 Mipmaptapping顺序排列,但有较多的纹理频道除外),并且可以纳入共同的Monte-Carlo路径追踪系统。我们用的方法在各种材料上展示了方法,导致复杂面面面面的外观和其他效果。