Image-based representations for efficient rendering and editing

Over the years, technological improvement has led to the ability to acquire, create and store vast amounts of geometrical and appearance data to use in graphical applications. Nevertheless, efficiently creating images when using such large amounts of data remains an ongoing topic of research, given the computational resources required. This dissertation will focus on a particular kind of algorithms in order to tackle this problem: image-based representations.
Entities represented in a virtual 3-dimensional world can be projected into a regular 2-dimensional grid to form an image. This results in a much more compact, albeit incomplete, representation of the original information, since an image is a piece-wise constant discretization of the underlying data. Nevertheless, computing properties of the 3-dimensional data via its 2-dimensional projection is much more efficient. Still, it is important to understand when and how to use these types of representations, since the discretization of the original data can lead to artifacts in the computed results.
This work will focus on three key aspects of computer-graphics algorithms where using appropriate image-based representations can result in increased performance: memory requirements, computational efficiency, and interactivity. To do so, it will describe image-based solutions to different relevant problems in the field of computer graphics. Firstly, by exploring how using 2-dimensional intermediate representation can reduce memory requirements for storing large static shadow information in comparison to state-of-the-art voxel representations. Secondly, a hierarchical image-based approach for efficiently computing diffraction patterns will be demonstrated, which can outperform FFT-based solutions. Lastly, an efficient interactive optimization method for editing disparity when creating stereographic images will be described.
These algorithms will be described and evaluated in detail, in order to give the reader insight into the usage of image-based representations.