ABSTRACT
The Azure Kinect represents the latest generation of Microsoft Kinect depth cameras. Of interest in this article is the depth and spatial accuracy of the Azure Kinect and how it compares to its predecessor, the Kinect v2. In one experiment, the two sensors are used to capture a planar whiteboard at 15 locations in a grid pattern with laser scanner data serving as ground truth. A set of histograms reveals the temporal-based random depth error inherent in each Kinect. Additionally, a two-dimensional cone of accuracy illustrates the systematic spatial error. At distances greater than 2.5 m, we find the Azure Kinect to have improved accuracy in both spatial and temporal domains as compared to the Kinect v2, while for distances less than 2.5 m, the spatial and temporal accuracies were found to be comparable. In another experiment, we compare the distribution of random depth error between each Kinect sensor by capturing a flat wall across the field of view in horizontal and vertical directions. We find the Azure Kinect to have improved temporal accuracy over the Kinect v2 in the range of 2.5 to 3.5 m for measurements close to the optical axis. The results indicate that the Azure Kinect is a suitable substitute for Kinect v2 in 3D scanning applications.
Subject(s)
Computer Systems , Light , Biomechanical PhenomenaABSTRACT
OBJECTIVE: To investigate the distribution of distal and lateral forces produced by orthodontic asymmetric headgear (AHG) using mathematical models to assess periodontal ligament (PDL) influence and to attempt to resolve apparent inconsistencies in the literature. MATERIALS AND METHODS: Mechanical models for AHG were constructed to calculate AHG force magnitudes and direction using the theory of elasticity. The PDL was simulated by elastic springs attached to the inner-bow terminals of the AHG. The total storage energy (E(t)) of the AHG and the supporting springs was integrated to evaluate the distal and lateral forces produced by minimizing E(t) (Castigliano's theorem). All analytical solutions were derived symbolically. RESULTS: The spring-supported headgear model (SSHG) predicted the magnitude and distribution of distal forces consistent with our data and the published data of others. The SSHG model revealed that the lateral forces delivered to the inner-bow terminals were not equal, and the spring constant (stiffness of the PDL) affected the magnitude and direction of the resultant lateral forces. Changing the stiffness of the PDL produced a greater biomechanical effect than did altering the face-bow design. The PDL spring model appeared to help resolve inconsistencies in the literature between laboratory in vitro experiments and clinical in vivo studies. CONCLUSION: Force magnitude and direction of AHG were predicted precisely using the present model and may be applied to improve the design of AHG to minimize unwanted lateral tooth movement.
