Vinodkumar Prasoon Kumar, Karabulut Dogus, Avots Egils, Ozcinar Cagri, Anbarjafari Gholamreza
iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia.
PwC Advisory, 00180 Helsinki, Finland.
Entropy (Basel). 2024 Mar 7;26(3):235. doi: 10.3390/e26030235.
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future.
计算机视觉、图形学和机器学习领域的研究小组已将大量注意力投入到三维物体重建、增强和配准领域。深度学习是人工智能中用于应对计算机视觉挑战的主要方法。然而，对三维数据进行深度学习存在明显障碍，且目前尚处于起步阶段。专门针对三维数据的深度学习已取得显著进展，提供了一系列解决这些问题的方法。本研究全面审视了深度学习方法的最新进展。我们研究了许多用于三维物体配准、增强和重建任务的基准模型。我们深入分析了它们的架构、优势和局限性。总之，本报告全面概述了三维深度学习的最新进展，并突出了未来需要解决的未决研究领域。
