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Abstract:
We
present an integrated approach for real-time performance prediction of volume
raycasting that we employ for load balancing and sampling resolution tuning. In
volume rendering, the usage of acceleration techniques such as empty space
skipping and early ray termination, among others, can cause significant
variations in rendering performance when users adjust the camera configuration
or transfer function. These variations in rendering times may result in
unpleasant effects such as jerky motions or abruptly reduced responsiveness
during interactive exploration. To avoid those effects, we propose an
integrated approach to adapt rendering parameters according to performance
needs. We assess performance-relevant data on-the-fly, for which we propose a
novel technique to estimate the impact of early ray termination. On the basis
of this data, we introduce a hybrid model, to achieve accurate predictions with
minimal computational footprint. Our hybrid model incorporates aspects from
analytical performance modeling and machine learning, with the goal to combine
their respective strengths. We show the applicability of our prediction model
for two different use cases: (1) to dynamically steer the sampling density in
object and/or image space and (2) to dynamically distribute the workload among
several different parallel computing devices. Our approach allows to reliably
meet performance requirements such as a user-defined frame rate, even in the
case of sudden large changes to the transfer function or the camera
orientation.
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