Analysis of the application of artificial intelligence for 3D scanning data processing

DOI: 10.31673/2412-9070.2025.027017

Abstract

This article explores the application of artificial intelligence (AI) in the processing of 3D scanning data as a promising direction in the field of digital technologies. The study focuses on a detailed examination of contemporary methods such as noise reduction, segmentation, and reconstruction of 3D objects using deep learning models. Particular attention is paid to how AI enhances accuracy, automates complex data analysis processes, and reduces the need for manual intervention in 3D workflows.
The research highlights the advantages of integrating AI into 3D data processing pipelines, including improvements in speed, precision, and scalability. These benefits are particularly valuable in sectors such as industrial visualization, architecture, medical diagnostics, and cultural heritage preservation. At the same time, the paper discusses existing limitations of AI models—such as their reliance on large annotated datasets, difficulties in generalizing to real-world scenarios, and high computational requirements.
A number of practical recommendations are proposed for the effective integration of AI into 3D scanning projects. These include selecting appropriate models based on specific project goals, ensuring data quality and diversity, adopting hybrid workflows that combine traditional algorithms with AI-based methods, and building scalable systems that are adaptable to dynamic environments. The use of both classical algorithms (e.g., Poisson reconstruction, ICP, SOR) and modern AI techniques demonstrates the need for a balanced, context-aware approach to 3D data processing.
The findings of this study offer valuable insights for researchers and practitioners aiming to optimize the accuracy and efficiency of 3D scanning technologies. The article concludes with an outlook on future research directions, such as the development of lightweight, domain-adaptive models, self supervised learning approaches, and comprehensive real-world datasets. These efforts are essential for overcoming current challenges and creating robust, generalizable AI systems capable of reliable performance in diverse application areas..

Keywords: artificial intelligence; 3D scanning; 3D data analysis; computer technologies; deep learning; machine learning; neural networks; data processing.