Decision tree for selecting software for creating augmented reality

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Потрашкова Л. В., Гмирак М. Д. № 2 (67) 42-50 Image Image

Multimedia projects with augmented reality (AR) today are provided with different AR-development tools. At the same time, due to the available variety of software, the problem of selecting a suitable tool arises.

The purpose of the study is to develop a methodology for selecting software for creating augmented reality, taking into account the existence of different types of AR-development tools.

The methodology proposed in the article includes the following steps:

Stage 1. Identifying of alternative tools of creating augmented reality and defining their types.

The analysis of software shows that various tools of AR-development can be divided into three groups: 1) advanced tools (ARKit, AR Core, Vuforia); 2) simpler tools (Meta Spark Studio and Adobe Aero); 3) tools for non-professionals (EyeJack та Artivive).

Stage 2. Formation of evaluation criteria for augmented reality tools.

Stage 3. Formulation of specific AR-project requirements for augmented reality tools.

To determine the requirements for AR-development tools, it is necessary to answer the following questions, in particular: 1. Do developers have the competence to create specialized apps for AR-visualization? 2. Will users be inclined to download a specia­lized app for AR-visualization? 3. Is there a need to upload large files to the AR-content?

Stage 4. Selection of augmented reality tool for a specific project.

To support the selection of augmented reality tool, a base of production rules is developed and a decision tree is built on this basis.

The proposed methodology allows determining the most suitable version of the software for a specific project, taking into account a number of characteristics of the software and parameters of the AR-project.

Keywords: augmented reality, software selection, decision tree, method of production diagnostics, selection criteria.

doi: 10.32403/1998-6912-2023-2-67-42-50


  • 1. Artivive. Knowledge Base. Retrieved from https://artivive.com/resources/ (data zvernennia 28.07.2023) (in English).
  • 2. Creating and managing projects in Meta Spark Studio. Retrieved from https://spark.meta.com/learn/articles/fundamentals/project-management (data zvernennia 28.07.2023) (in English).
  • 3. EyeJack Creator. Retrieved from https://creator.eyejackapp.com/ (data zvernennia 28.07.2023) (in English).
  • 4. Vuforia Engine Overview. Retrieved from https://developer.vuforia.com/library/getting-star­ted/vuforia-features (data zvernennia 28.07.2023) (in English).
  • 5. Boonbrahm, S., Boonbrahm, P., & Kaewrat, C. (2020). The Use of Marker-Based Augmen­ted Reality in Space Measurement: Procedia Manufacturing, 42, 337–343. Doi: https://doi.org/10.1016/j.promfg.2020.02.081 (in English).
  • 6. Kolla, S. S. V. K., Sanchez, A., & Plapper, P. (2021). Comparing software frameworks of Augmented Reality solutions for manufacturing: Procedia Manufacturing, 55, 312–318. Doi: https://doi.org/10.1016/j.promfg.2021.10.044/ (in English).
  • 7. Grodotzki, J., Müller, B. T., & Tekkaya, A. E. (2023). Introducing a general-purpose augmen­ted reality platform for the use in engineering education: Advances in Industrial and Manufacturing Engineering, 6, 100116. Doi: https://doi.org/10.1016/j.aime.2023.100116 (in English).
  • 8. Stepaniuk, O. S., & Sichko, T. V. Porivnialnyi analiz instrumentiv dlia pobudovy dodatkiv z dopovnenoiu realnistiu. Kompiuterni tekhnolohii obrobky danykh : materialy vseukr. nauk.-prakt. konf. (Vinnytsia, 10 hrudnia 2021), 98−101. Retrieved from https://jktod.donnu.edu.ua/article/view/11662/ (in Ukrainian).