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Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average FI score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next, we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance. 

The traditional freehand placement of an external ventricular drain (EVD) relies on empirical craniometric landmarks to guide the craniostomy and subsequent passage of the EVD catheter. The diameter and trajectory of the craniostomy physically limit the possible trajectories that can be achieved during the passage of the catheter. In this study, the authors implemented a mixed reality–guided craniostomy procedure to evaluate the benefit of an optimally drilled craniostomy to the accurate placement of the catheter. Optical marker–based tracking using an OptiTrack system was used to register the brain ventricular hologram and drilling guidance for craniostomy using a HoloLens 2 mixed reality headset. A patient-specific 3D-printed skull phantom embedded with intracranial camera sensors was developed to automatically calculate the EVD accuracy for evaluation. User trials consisted of one blind and one mixed reality–assisted craniostomy followed by a routine, unguided EVD catheter placement for each of two different drill bit sizes. A total of 49 participants were included in the study (mean age 23.4 years, 59.2% female). The mean distance from the catheter target improved from 18.6 ± 12.5 mm to 12.7 ± 11.3 mm (p = 0.0008) using mixed reality guidance for trials with a large drill bit and from 19.3 ± 12.7 mm to 10.1 ± 8.4 mm with a small drill bit (p < 0.0001). Accuracy using mixed reality was improved using a smaller diameter drill bit compared with a larger bit (p = 0.039). Overall, the majority of the participants were positive about the helpfulness of mixed reality guidance and the overall mixed reality experience. Appropriate indications and use cases for the application of mixed reality guidance to neurosurgical procedures remain an area of active inquiry. While prior studies have demonstrated the benefit of mixed reality–guided catheter placement using predrilled craniostomies, the authors demonstrate that real-time quantitative and visual feedback of a mixed reality–guided craniostomy procedure can independently improve procedural accuracy and represents an important tool for trainee education and eventual clinical implementation. 

We present the method of direct van der Waals simulation (DVS) to study computationally flows with liquid-vapor phase transformations. Our approach is based on a discretization of the Navier-Stokes-Korteweg equations, which couple flow dynamics with van der Waals’ nonequilibrium thermodynamic theory of phase transformations, and opens an opportunity for first-principles simulation of a wide range of boiling and cavitating flows. The proposed algorithm enables unprecedented simulations of the Navier-Stokes-Korteweg equations involving cavitating flows at strongly under-critical conditions and 𝒪(10⁵) Reynolds number. The proposed technique provides a pathway for a fundamental understanding of phase-transforming flows with multiple applications in science, engineering, and medicine.