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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08137 |
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| _version_ | 1866909250601615360 |
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| author | Bai, Yonge Wong, LikHang Twan, TszYin |
| author_facet | Bai, Yonge Wong, LikHang Twan, TszYin |
| contents | This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes, highlighting Neural Radiance Fields (NeRFs), Latent Diffusion Models (LDM), and 3D Gaussian Splatting. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving field. We provide a comprehensive overview of the fundamental in DL-driven 3D scene reconstruction, offering insights into their potential applications and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on Fundamental Deep Learning 3D Reconstruction Techniques Bai, Yonge Wong, LikHang Twan, TszYin Computer Vision and Pattern Recognition Graphics This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes, highlighting Neural Radiance Fields (NeRFs), Latent Diffusion Models (LDM), and 3D Gaussian Splatting. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving field. We provide a comprehensive overview of the fundamental in DL-driven 3D scene reconstruction, offering insights into their potential applications and limitations. |
| title | Survey on Fundamental Deep Learning 3D Reconstruction Techniques |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2407.08137 |