<p>To correctly reproduce the appearance of
different objects in computer graphics applications, numerous lighting models
have been proposed over the past several decades. These models are among the
most important components in the modern graphics pipeline since they decide the
final pixel color shown in the generated images. More physically valid
parameters and functions have been introduced into recent models. These parameters
expanded the range of materials that can be represented and made virtual scenes
more realistic, but they also made the lighting models more complex and
dependent on measured data.</p>
<p>Artificial neural networks, or neural
networks are famous for their ability to deal with complex data and to
approximate arbitrary functions. They have been adopted by many data-driven
approaches for computer graphics and proven to be effective. Furthermore,
neural networks have also been used by the artists for creative works and
proven to have the ability of supporting creation of visual effects, animation
and computational arts. Therefore, it is reasonable to consider artificial
neural networks as potential tools for representing lighting models. Since
shaders are used for general-purpose computing, neural networks can be further
combined with modern graphics pipeline using shader implementation. </p>
<p>In this research, the possibilities of
shader-based neural networks to be used as an alternative to traditional
lighting models are explored. Fully connected neural networks are implemented
in fragment shader to reproduce lighting results in the graphics pipeline, and
trained in compute shaders. Implemented networks are proved to be able to
approximate mathematical lighting models. In this thesis, experiments are
described to prove the ability of shader-based neural networks, to explore the
proper network architecture and settings for different lighting models. Further
explorations of possibilities of manually editing parameters are also
described. Mean-square errors and runtime are taken as measurements of success
to evaluate the experiments. Rendered images are also reported for visual
comparison and evaluation.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12220133 |
| Date | 01 May 2020 |
| Creators | Qin He (8784458) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Learning_Lighting_Models_with_Shader-Based_Neural_Networks/12220133 |
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