Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper
Author:
Vinodkumar Prasoon Kumar1, Karabulut Dogus1, Avots Egils1, Ozcinar Cagri1, Anbarjafari Gholamreza1234ORCID
Affiliation:
1. iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia 2. PwC Advisory, 00180 Helsinki, Finland 3. iVCV OÜ, 51011 Tartu, Estonia 4. Institute of Higher Education, Yildiz Technical University, Beşiktaş, Istanbul 34349, Turkey
Abstract
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.
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