This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDespite their age, ray-based rendering methods are still a very active field of research
with many challenges when it comes to interactive visualization. In this thesis, we
present our work on Guided High-Quality Rendering, Foveated Ray Tracing for Head Mounted Displays and Hash-based Hierarchical Caching and Layered Filtering.
Our system for Guided High-Quality Rendering allows for guiding the sampling
rate of ray-based rendering methods by a user-specified Region of Interest (RoI).
We propose two interaction methods for setting such an RoI when using a large
display system and a desktop display, respectively. This makes it possible to compute
images with a heterogeneous sample distribution across the image plane. Using
such a non-uniform sample distribution, the rendering performance inside the RoI
can be significantly improved in order to judge specific image features. However, a
modified scheduling method is required to achieve sufficient performance. To solve
this issue, we developed a scheduling method based on sparse matrix compression,
which has shown significant improvements in our benchmarks. By filtering the
sparsely sampled image appropriately, large brightness variations in areas outside
the RoI are avoided and the overall image brightness is similar to the ground truth
early in the rendering process.
When using ray-based methods in a VR environment on head-mounted display de vices, it is crucial to provide sufficient frame rates in order to reduce motion sickness.
This is a challenging task when moving through highly complex environments and
the full image has to be rendered for each frame. With our foveated rendering sys tem, we provide a perception-based method for adjusting the sample density to the
user’s gaze, measured with an eye tracker integrated into the HMD. In order to
avoid disturbances through visual artifacts from low sampling rates, we introduce
a reprojection-based rendering pipeline that allows for fast rendering and temporal
accumulation of the sparsely placed samples. In our user study, we analyse the im pact our system has on visual quality. We then take a closer look at the recorded
eye tracking data in order to determine tracking accuracy and connections between
different fixation modes and perceived quality, leading to surprising insights.
For previewing global illumination of a scene interactively by allowing for free scene
exploration, we present a hash-based caching system. Building upon the concept
of linkless octrees, which allow for constant-time queries of spatial data, our frame work is suited for rendering such previews of static scenes. Non-diffuse surfaces are
supported by our hybrid reconstruction approach that allows for the visualization of
view-dependent effects. In addition to our caching and reconstruction technique, we
introduce a novel layered filtering framework, acting as a hybrid method between
path space and image space filtering, that allows for the high-quality denoising of
non-diffuse materials. Also, being designed as a framework instead of a concrete
filtering method, it is possible to adapt most available denoising methods to our
layered approach instead of relying only on the filtering of primary hitpoints