Creating realistic virtual assets is a time-consuming process: it usually
involves an artist designing the object, then spending a lot of effort on
tweaking its appearance. Intricate details and certain effects, such as
subsurface scattering, elude representation using real-time BRDFs, making it
impossible to fully capture the appearance of certain objects. Inspired by the
recent progress of neural rendering, we propose an approach for capturing
real-world objects in everyday environments faithfully and fast. We use a novel
neural representation to reconstruct volumetric effects, such as translucent
object parts, and preserve photorealistic object appearance. To support
real-time rendering without compromising rendering quality, our model uses a
grid of features and a small MLP decoder that is transpiled into efficient
shader code with interactive framerates. This leads to a seamless integration
of the proposed neural assets with existing mesh environments and objects.
Thanks to the use of standard shader code rendering is portable across many
existing hardware and software systems.

本研究提出了一种使用神经渲染的方法，利用新颖的神经表征重构对象的透明部分，并保留物体的逼真外观，使用非常高效的着色器代码支持实时渲染，并通过标准的着色器代码生成，实现与现有硬件和软件系统的无缝集成。