  Neural Radiance Fields (NeRF) with hybrid representations have shown
impressive capabilities in reconstructing scenes for view synthesis, delivering
high efficiency. Nonetheless, their performance significantly drops with sparse
view inputs, due to the issue of overfitting. While various regularization
strategies have been devised to address these challenges, they often depend on
inefficient assumptions or are not compatible with hybrid models. There is a
clear need for a method that maintains efficiency and improves resilience to
sparse views within a hybrid framework. In this paper, we introduce an accurate
and efficient few-shot neural rendering method named Spatial Annealing
smoothing regularized NeRF (SANeRF), which is specifically designed for a
pre-filtering-driven hybrid representation architecture. We implement an
exponential reduction of the sample space size from an initially large value.
This methodology is crucial for stabilizing the early stages of the training
phase and significantly contributes to the enhancement of the subsequent
process of detail refinement. Our extensive experiments reveal that, by adding
merely one line of code, SANeRF delivers superior rendering quality and much
faster reconstruction speed compared to current few-shot NeRF methods. Notably,
SANeRF outperforms FreeNeRF by 0.3 dB in PSNR on the Blender dataset, while
achieving 700x faster reconstruction speed.
