  Modern video games are rapidly growing in size and scale, and to create rich
and interesting environments, a large amount of content is needed. As a
consequence, often several thousands of detailed 3D assets are used to create a
single scene. As each asset's polygon mesh can contain millions of polygons,
the number of polygons that need to be drawn every frame may exceed several
billions. Therefore, the computational resources often limit how many detailed
objects that can be displayed in a scene. To push this limit and to optimize
performance one can reduce the polygon count of the assets when possible.
Basically, the idea is that an object at farther distance from the capturing
camera, consequently with relatively smaller screen size, its polygon count may
be reduced without affecting the perceived quality. Level of Detail (LOD)
refers to the complexity level of a 3D model representation. The process of
removing complexity is often called LOD reduction and can be done automatically
with an algorithm or by hand by artists. However, this process may lead to
deterioration of the visual quality if the different LODs differ significantly,
or if LOD reduction transition is not seamless. Today the validation of these
results is mainly done manually requiring an expert to visually inspect the
results. However, this process is slow, mundane, and therefore prone to error.
Herein we propose a method to automate this process based on the use of deep
convolutional networks. We report promising results and envision that this
method can be used to automate the process of LOD reduction testing and
validation.
