RING-NeRF: A Versatile Architecture based on Residual Implicit Neural Grids

Since their introduction, Neural Fields have become very popular for 3D reconstruction and new view synthesis. Recent researches focused on accelerating the process, as well as improving the robustness to variation of the observation distance and limited number of supervised viewpoints. However, those approaches often led to dedicated solutions that cannot be easily combined. To tackle this issue, we introduce a new simple but efficient architecture named RING-NeRF, based on Residual Implicit Neural Grids, that provides a control on the level of detail of the mapping function between the scene and the latent spaces. Associated with a distance-aware forward mapping mechanism and a continuous coarse-to-fine reconstruction process, our versatile architecture demonstrates both fast training and state-of-the-art performances in terms of: (1) anti-aliased rendering, (2) reconstruction quality from few supervised viewpoints, and (3) robustness in the absence of appropriate scene-specific initialization for SDF-based NeRFs. We also demonstrate that our architecture can dynamically add grids to increase the details of the reconstruction, opening the way to adaptive reconstruction.