Neural directional distance field object representation for
  uni-directional path-traced rendering
Annada Prasad Behera Subhankar Mishra
Faster rendering of synthetic images is a core problem in the field of
computer graphics. Rendering algorithms, such as path-tracing is dependent on
parameters like size of the image, number of light bounces, number of samples
per pixel, all of which, are fixed if one wants to obtain a image of a desired
quality. It is also dependent on the size and complexity of the scene being
rendered. One of the largest bottleneck in rendering, particularly when the
scene is very large, is querying for objects in the path of a given ray in the
scene. By changing the data type that represents the objects in the scene, one
may reduce render time, however, a different representation of a scene requires
the modification of the rendering algorithm. In this paper, (a) we introduce
directed distance field, as a functional representation of a object; (b) how
the directed distance functions, when stored as a neural network, be optimized
and; (c) how such an object can be rendered with a modified path-tracing
algorithm.