Abstract
Recent advances in Neural Fields mostly rely on developing task-specific supervision which often complicates the models. Rather than developing hard-to-combine and specific modules, another approach generally overlooked is to directly inject generic priors on the scene representation (also called inductive biases) into the NeRF architecture. Based on this idea, we propose the RING-NeRF architecture which includes two inductive biases: a continuous multi-scale representation of the scene and an invariance of the decoder’s latent space over spatial and scale domains. We also design a single reconstruction process that takes advantage of those inductive biases and experimentally demonstrates on-par performances in terms of quality with dedicated architecture on multiple tasks (anti-aliasing, few view reconstruction, SDF reconstruction without scene-specific initialization) while being more efficient. Moreover, RING-NeRF has the distinctive ability to dynamically increase the resolution of the model, opening the way to adaptive reconstruction. Project page can be found at: https://cea-list.github.io/RING-NeRF
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Notes
- 1.
With SDF, an additional Eikonal loss is also being optimized for SDF regularization.
- 2.
A virtual grid of LOD \(L \in \mathbb {R}^+\) corresponds to a grid of resolution \(f^Lb\) that is not explicitly stored in memory but whose elements can be computed from other grids.
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This publication was made possible by the use of the CEA List FactoryIA supercomputer, financially supported by the Ile-de-France Regional Council.
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Petit, D., Bourgeois, S., Pavel, D., Gay-Bellile, V., Chabot, F., Barthe, L. (2025). RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15093. Springer, Cham. https://doi.org/10.1007/978-3-031-72761-0_9
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