Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper
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 cha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/26/3/235 |
| _version_ | 1797241188775886848 |
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| author | Prasoon Kumar Vinodkumar Dogus Karabulut Egils Avots Cagri Ozcinar Gholamreza Anbarjafari |
| author_facet | Prasoon Kumar Vinodkumar Dogus Karabulut Egils Avots Cagri Ozcinar Gholamreza Anbarjafari |
| author_sort | Prasoon Kumar Vinodkumar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:19:22Z |
| format | Article |
| id | doaj.art-9f30129759cf4b0e97d9921ac97b6c17 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-24T18:19:22Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9f30129759cf4b0e97d9921ac97b6c172024-03-27T13:36:55ZengMDPI AGEntropy1099-43002024-03-0126323510.3390/e26030235Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review PaperPrasoon Kumar Vinodkumar0Dogus Karabulut1Egils Avots2Cagri Ozcinar3Gholamreza Anbarjafari4iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, EstoniaiCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, EstoniaiCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, EstoniaiCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, EstoniaiCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, EstoniaThe 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.https://www.mdpi.com/1099-4300/26/3/235deep learning3D reconstruction3D augmentation3D registrationpoint cloudvoxel |
| spellingShingle | Prasoon Kumar Vinodkumar Dogus Karabulut Egils Avots Cagri Ozcinar Gholamreza Anbarjafari Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper Entropy deep learning 3D reconstruction 3D augmentation 3D registration point cloud voxel |
| title | Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper |
| title_full | Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper |
| title_fullStr | Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper |
| title_full_unstemmed | Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper |
| title_short | Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper |
| title_sort | deep learning for 3d reconstruction augmentation and registration a review paper |
| topic | deep learning 3D reconstruction 3D augmentation 3D registration point cloud voxel |
| url | https://www.mdpi.com/1099-4300/26/3/235 |
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