3D Imaging Group, Faculty of Computing Sciences and Engineering
Abstract
The research work presented in this thesis has approached the task of accelerating the
generation of photo-realistic integral images produced by integral ray tracing.
Ray tracing algorithm is a computationally exhaustive algorithm, which spawns one ray
or more through each pixel of the pixels forming the image, into the space containing
the scene. Ray tracing integral images consumes more processing time than normal
images. The unique characteristics of the 3D integral camera model has been analysed
and it has been shown that different coherency aspects than normal ray tracing can be
investigated in order to accelerate the generation of photo-realistic integral images.
The image-space coherence has been analysed describing the relation between rays and
projected shadows in the scene rendered. Shadow cache algorithm has been adapted in
order to minimise shadow intersection tests in integral ray tracing. Shadow intersection
tests make the majority of the intersection tests in ray tracing. Novel pixel-tracing
styles are developed uniquely for integral ray tracing to improve the image-space
coherence and the performance of the shadow cache algorithm. Acceleration of the
photo-realistic integral images generation using the image-space coherence information
between shadows and rays in integral ray tracing has been achieved with up to 41 % of
time saving. Also, it has been proven that applying the new styles of pixel-tracing does
not affect of the scalability of integral ray tracing running over parallel computers.
The novel integral reprojection algorithm has been developed uniquely through
geometrical analysis of the generation of integral image in order to use the tempo-spatial
coherence information within the integral frames. A new derivation of integral
projection matrix for projecting points through an axial model of a lenticular lens has
been established. Rapid generation of 3D photo-realistic integral frames has been
achieved with a speed four times faster than the normal generation
