The concept of using multiple deep images,under a variety of different names, has been explored as a possible acceleration approach for finding raygeometry intersections. We leverage recent advances in deep image proc...The concept of using multiple deep images,under a variety of different names, has been explored as a possible acceleration approach for finding raygeometry intersections. We leverage recent advances in deep image processing from order independent transparency for fast building of a compound deep image(CDI) using a coherent memory format well suited for raycasting. We explore the use of a CDI and raycasting for the problem of determining distance between virtual point lights(VPLs) and geometry for indirect lighting,with the key raycasting step being a small fraction of total frametime.展开更多
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 suc...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 performancerelevant 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.展开更多
文摘The concept of using multiple deep images,under a variety of different names, has been explored as a possible acceleration approach for finding raygeometry intersections. We leverage recent advances in deep image processing from order independent transparency for fast building of a compound deep image(CDI) using a coherent memory format well suited for raycasting. We explore the use of a CDI and raycasting for the problem of determining distance between virtual point lights(VPLs) and geometry for indirect lighting,with the key raycasting step being a small fraction of total frametime.
基金We would like to thank the German Research Foundation(DFG)for supporting the project within project A02 of SFB/Transregio 161.
文摘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 performancerelevant 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.
