Assessment of 3D Models Placement Methods in Augmented Reality

Abstract

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study

“…All scanned models were exported as standard tessellation language (.stl) files. Comparisons were made for Hausdorff's distance (HD) [ 30 ] and root mean squared error (RMSE) [ 31 ] by considering the dataset derived from the benchtop scanner as standard. HD is employed to assess the maximum deviation between two surfaces, such as dental models, encompassing both overestimation and underestimation.…”

“…HD is employed to assess the maximum deviation between two surfaces, such as dental models, encompassing both overestimation and underestimation. In contrast, RMSE calculates the average disparity between corresponding points of the two surfaces, highlighting the overall accuracy of the scan without distinguishing between overestimation and underestimation [ 30 , 31 ].…”

An AR Application for the Efficient Construction of Water Pipes Buried Underground

“…By associating appropriate information with the created water pipe model, information corresponding to the selected water pipe model can be displayed on the application screen. Figure 8 shows the result of importing a curved pipe created with Probuilder into the development environment Unity [5,6] The water pipe models used in this system are 3D objects created using Metamesh, a package that allows the creation of simple 3D models in Unity, and Probuilder, a package that allows more detailed editing than Metamesh. The 3D objects created with Metamesh used in this system were created from Cylinder, a cylindrical 3D model in Metamesh, in order to simply show a water pipe.…”

“…By associating appropriate information with the created water pipe model, information corresponding to the selected water pipe model can be displayed on the application screen. Figure 8 shows the result of importing a curved pipe created with Probuilder into the development environment Unity [5,6]…”

Green Tech and Human Dynamics: Transforming Indonesia’s Waste Industry with VR, AR, and Renewable Energy Innovations

“…Companies must lessen their reliance on ecologically unfriendly technology by implementing virtual technology that utilizes renewable energy derived from nature, resulting in a 20% decrease in carbon emissions (El Barhoumi et al, 2022). Medium and large enterprises that effectively manage their industrial waste are the ones who actively decrease their carbon emissions.…”