Neural Radiance Field Image Refinement through End-to-End Sampling Point Optimization
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914979111763968 |
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| author | Ohta, Kazuhiro Ono, Satoshi |
| author_facet | Ohta, Kazuhiro Ono, Satoshi |
| contents | Neural Radiance Field (NeRF), capable of synthesizing high-quality novel viewpoint images, suffers from issues like artifact occurrence due to its fixed sampling points during rendering. This study proposes a method that optimizes sampling points to reduce artifacts and produce more detailed images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14958 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Radiance Field Image Refinement through End-to-End Sampling Point Optimization Ohta, Kazuhiro Ono, Satoshi Computer Vision and Pattern Recognition Machine Learning Neural Radiance Field (NeRF), capable of synthesizing high-quality novel viewpoint images, suffers from issues like artifact occurrence due to its fixed sampling points during rendering. This study proposes a method that optimizes sampling points to reduce artifacts and produce more detailed images. |
| title | Neural Radiance Field Image Refinement through End-to-End Sampling Point Optimization |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.14958 |