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| Hlavní autoři: | , , , , , |
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| Médium: | Preprint |
| Vydáno: |
2024
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| Témata: | |
| On-line přístup: | https://arxiv.org/abs/2402.07310 |
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| _version_ | 1866916495482683392 |
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| author | Passos, Leandro A. Rodrigues, Douglas Jodas, Danilo Costa, Kelton A. P. Adeel, Ahsan Papa, João Paulo |
| author_facet | Passos, Leandro A. Rodrigues, Douglas Jodas, Danilo Costa, Kelton A. P. Adeel, Ahsan Papa, João Paulo |
| contents | This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent neural models, one responsible for producing the volumetric densities and the other the colors used to render the scene. Experimental results show that BioNeRF outperforms state-of-the-art results concerning a quality measure that encodes human perception in two datasets: real-world images and synthetic data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07310 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis Passos, Leandro A. Rodrigues, Douglas Jodas, Danilo Costa, Kelton A. P. Adeel, Ahsan Papa, João Paulo Computer Vision and Pattern Recognition Machine Learning This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent neural models, one responsible for producing the volumetric densities and the other the colors used to render the scene. Experimental results show that BioNeRF outperforms state-of-the-art results concerning a quality measure that encodes human perception in two datasets: real-world images and synthetic data. |
| title | BioNeRF: Biologically Plausible Neural Radiance Fields for View Synthesis |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.07310 |