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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.08012 |
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| _version_ | 1866914830298906624 |
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| author | Skorokhodov, Vsevolod Drozdova, Darya Yudin, Dmitry |
| author_facet | Skorokhodov, Vsevolod Drozdova, Darya Yudin, Dmitry |
| contents | Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector representations, called uSF, which allows the model to predict not only color and semantic label of each point, but also estimate the corresponding values of uncertainty. We show that with a small number of images available for training, a model quantifying uncertainty performs better than a model without such functionality. Code of the uSF approach is publicly available at https://github.com/sevashasla/usf/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08012 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | uSF: Learning Neural Semantic Field with Uncertainty Skorokhodov, Vsevolod Drozdova, Darya Yudin, Dmitry Computer Vision and Pattern Recognition Artificial Intelligence Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector representations, called uSF, which allows the model to predict not only color and semantic label of each point, but also estimate the corresponding values of uncertainty. We show that with a small number of images available for training, a model quantifying uncertainty performs better than a model without such functionality. Code of the uSF approach is publicly available at https://github.com/sevashasla/usf/. |
| title | uSF: Learning Neural Semantic Field with Uncertainty |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2312.08012 |