Paper Session 1: Displays

Target and steering-based techniques are two common approaches to travel in consumer VR applications. In this paper, we present two within-subject studies that employ a prior dual-task methodology to evaluate and compare the cognitive loads, travel performances, and simulator sickness of three common target-based travel techniques and three common steering-based travel techniques. We also present visual meta-analyses comparing our results to prior results using the same dual-task methodology. Based on our results and meta-analyses, we present several design suggestions for travel techniques based on various aspects of user experiences.

Various redirected walking (RDW) techniques have been proposed, which unwittingly manipulate the mapping from the user’s physical locomotion to motions of the virtual camera. Thereby, RDW techniques guide users on physical paths with the goal to keep them inside a limited tracking area, whereas users perceive the illusion of being able to walk infinitely in the virtual environment. However, the inconsistency between the user’s virtual and physical location hinders passive haptic feedback when the user interacts with virtual objects, which are represented by physical props in the real environment.

In this paper, we present a novel reinforcement learning approach towards RDW with passive haptics. With a novel dense reward function, our method learns to jointly consider physical boundary avoidance and consistency of user-object positioning between virtual and physical spaces. The weights of reward and penalty terms in the reward function are dynamically adjusted to adaptively balance term impacts during the walking process. Experimental results demonstrate the advantages of our technique in comparison to previous approaches. Finally, the code of our technique is provided as
an open-source solution.

Virtual Reality (VR) is a promising technology platform for immersive visual analytics. However, the design space of VR analytics interface design is vast and difficult to explore using traditional A/B comparisons in formal or informal controlled experiments—a fundamental part of an iterative design process. A key factor that complicates such comparisons is the dataset. Exposing participants to the same dataset in all conditions introduces an unavoidable learning effect. On the other hand, using different datasets for all experimental conditions introduces the dataset itself as an uncontrolled variable, which reduces internal validity to an unacceptable degree. In this paper, we propose to rectify this problem by introducing a generative process for synthesizing clustered datasets for VR analytics experiments. This process generates datasets that are distinct while simultaneously allowing systematic comparisons in experiments. A key advantage is that these datasets can then be used in iterative design processes. In a two-part experiment, we show the validity of the generative process and demonstrate how new insights in VR-based visual analytics can be gained using synthetic datasets.

In recent years, medical simulators have been widely applied to a broad range of surgery training tasks. However, most of the existing surgery simulators can only provide limited immersive environments with a few pre-processed organ models, while ignoring the instant modeling of various personalized clinical cases, which brings substantive differences between training experiences and real surgery situations. To this end, we present a virtual reality (VR) based surgery simulation system for personalized percutaneous coronary intervention (PCI). The simulation system can directly take patient-specific clinical data as input and generate virtual 3D intervention scenarios. Specially, we introduce a fiber-based patient-specific cardiac dynamic model to simulate the nonlinear deformation among the multiple layers of the cardiac structure, which can well respect and correlate the atriums, ventricles and vessels, and thus gives rise to more effective visualization and interaction. Meanwhile, we design a tracking and haptic feedback hardware, which can enable users to manipulate physical intervention instruments and interact with virtual scenarios. We conduct quantitative analysis on deformation precision and modeling efficiency, and evaluate the simulation system based on the user studies from 16 cardiologists and 20 intervention trainees, comparing it to traditional desktop intervention simulators. The results confirm that our simulation system can provide a better user experience, and is a suitable platform for PCI surgery training and rehearsal.