Congratulations to Duke team for Honorable Mention at the 2016 IEEE VR Conference

A team of Duke researchers, David Zielinski, Hrishikesh Rao, Nick Potter, Lawrence Applebaum, and Regis Kopper, represented Duke at the 2016 IEEE VR Conference, which took place March 19-23 in Greenville, NC. This year was the 26th year this premier international conference and exhibition took place, featuring some of the most innovative research, brightest minds, and top companies in virtual reality technology.

divepic2 The Duke team presented their latest research as a poster on “Evaluating the Effects of Image Persistence on Dynamic Target Acquisition in Low Frame Rate Virtual Environments.”Out of 84 poster presentations, the team won the honorable mention for best poster award. This places our team of Duke research at the top of cutting edge virtual reality technology advancements. A big congratulations to them!

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Their presentation, which was also featured as a full paper at the Symposium on 3D User Interfaces, was on recent research that analyzes a visual display technique for low frame rate virtual environments called low persistence (LP). Especially interesting to study is its difference to the low frame rate high persistence technique (HP). In the HP technique, the same rendered frame gets repeated a number of times until a new frame is generated—a process that we all see when running complex games in slow computers. With the LP technique, when a frame is generated, rather than showing it multiple times, a black frame is shown instead while waiting for the next new frame to be generated. To learn more about the LP technique, researchers at Duke evaluated user learning and performance during a target acquisition task. This task is similar to a shotgun trap shooting simulation, where the user has to acquire targets that were moving along several different trajectories. The results concluded that the LP technique may be just as useful as the low frame rate high persistence (HP) technique. The LP condition approaches high frame rate performance within certain classes of target trajectories, and user learning was similar in the LP and high frame system.

For more information, check out the poster abstract and the full paper.