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.

我们介绍了一种名为 RING-NeRF 的新的简单而高效的体系结构，基于 Residual Implicit Neural Grids，用于控制场景和潜在空间之间映射函数的细节级别，并结合距离感知的正向映射机制和连续的粗 - 细重建过程，在快速训练和性能方面展示出先进的表现，包括：抗锯齿渲染，从少数监督视点的重建质量以及在缺乏合适的场景特定初始化的情况下对基于 SDF 的 NeRF 的鲁棒性。我们还展示了我们的架构可以动态添加网格以增加重建的细节，为自适应重建铺平了道路。