Computer Science > Computer Vision and Pattern Recognition
arXiv:2401.06191 (cs)
[Submitted on 11 Jan 2024 (v1), last revised 17 Jul 2024 (this version, v2)]
Title:TriNeRFLet: A Wavelet Based Triplane NeRF Representation
View a PDF of the paper titled TriNeRFLet: A Wavelet Based Triplane NeRF Representation, by Rajaei Khatib and Raja Giryes
View PDFHTML (experimental)Abstract:In recent years, the neural radiance field (NeRF) model has gained popularity due to its ability to recover complex 3D scenes. Following its success, many approaches proposed different NeRF representations in order to further improve both runtime and performance. One such example is Triplane, in which NeRF is represented using three 2D feature planes. This enables easily using existing 2D neural networks in this framework, e.g., to generate the three planes. Despite its advantage, the triplane representation lagged behind in its 3D recovery quality compared to NeRF solutions. In this work, we propose TriNeRFLet, a 2D wavelet-based multiscale triplane representation for NeRF, which closes the 3D recovery performance gap and is competitive with current state-of-the-art methods. Building upon the triplane framework, we also propose a novel super-resolution (SR) technique that combines a diffusion model with TriNeRFLet for improving NeRF resolution.
| Comments: | Accepted to ECCV 2024. Webpage link:this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2401.06191 [cs.CV] |
| (orarXiv:2401.06191v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2401.06191 arXiv-issued DOI via DataCite |
Submission history
From: Rajaei Khatib [view email][v1] Thu, 11 Jan 2024 11:50:36 UTC (10,836 KB)
[v2] Wed, 17 Jul 2024 21:14:34 UTC (16,354 KB)
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View a PDF of the paper titled TriNeRFLet: A Wavelet Based Triplane NeRF Representation, by Rajaei Khatib and Raja Giryes
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