SNeS: learning probably symmetric neural surfaces from incomplete data
We present a method for the accurate 3D reconstruction of partly-symmetric objects. We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF). A major shortcoming of such approaches is that they fail to reconstruct any part of the objec...
| Autores principales: | , , , |
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| Formato: | Conference item |
| Lenguaje: | English |
| Publicado: |
Springer
2022
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