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.

翻译：自神经场提出以来，其在三维重建和新视角合成领域备受关注。近期研究主要聚焦于加速处理过程，以及提升对观测距离变化和有限监督视角数量的鲁棒性。然而，这些方法往往导致专用解决方案难以灵活组合。为解决该问题，我们提出一种名为RING-NeRF的简洁高效架构，该架构基于残差隐式神经网格，能够控制场景与潜在空间之间映射函数的细节层次。结合距离感知的前向映射机制与连续由粗到精的重建过程，我们的通用架构在以下方面展现出快速训练与当前最优性能：（1）抗锯齿渲染；（2）基于有限监督视角的重建质量；（3）在缺乏针对SDF类NeRF的特定场景初始化时的鲁棒性。我们还证明了该架构能够动态添加网格以提升重建细节，从而为自适应重建开辟了道路。