Détail du document
Identifiant
oai:arXiv.org:2403.14412
Sujet
Computer Science - Computer Vision...Auteur
Bonotto, Matteo Sarrocco, Luigi Evangelista, Daniele Imperoli, Marco Pretto, AlbertoCatégorie
Computer Science
Année
2024
Date de référencement
27/03/2024
Mots clés
few-shot views nerfRésumé
Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available.
When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering.
Regularization is a valid solution that helps NeRF generalization.
On the other hand, each of the most recent NeRF regularization techniques aim to mitigate a specific rendering problem.
Starting from this observation, in this paper we propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each.
In particular, we regularize single and neighboring rays distributions and we add a smoothness term to regularize near geometries.
After these geometric approaches, we propose to exploit Lipschitz regularization to both NeRF density and color networks and to use encoding masks for input features regularization.
We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets.
We also present an ablation study on the LLFF and NeRF-Synthetic datasets that support the choices made.
We release with this paper the open-source implementation of our framework.
;Comment: This paper has been accepted for publication at the 2024 International Conference on 3D Vision (3DV)
Bonotto, Matteo,Sarrocco, Luigi,Evangelista, Daniele,Imperoli, Marco,Pretto, Alberto, 2024, CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis
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