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.

提出了一种名为 SANeRF 的准确高效的小样本神经渲染方法，能够在混合表示架构中提供更好的渲染质量和更快的重建速度。

高效少样本神经渲染的空间退火平滑化

Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering

Neural Radiance Field (NeRF) technology has made significant strides in
creating novel viewpoints. However, its effectiveness is hampered when working
with sparsely available views, often leading to performance dips due to
overfitting. FreeNeRF attempts to overcome this limitation by integrating
implicit geometry regularization, which incrementally improves both geometry
and textures. Nonetheless, an initial low positional encoding bandwidth results
in the exclusion of high-frequency elements. The quest for a holistic approach
that simultaneously addresses overfitting and the preservation of
high-frequency details remains ongoing. This study introduces a novel feature
matching based sparse geometry regularization module. This module excels in
pinpointing high-frequency keypoints, thereby safeguarding the integrity of
fine details. Through progressive refinement of geometry and textures across
NeRF iterations, we unveil an effective few-shot neural rendering architecture,
designated as SGCNeRF, for enhanced novel view synthesis. Our experiments
demonstrate that SGCNeRF not only achieves superior geometry-consistent
outcomes but also surpasses FreeNeRF, with improvements of 0.7 dB and 0.6 dB in
PSNR on the LLFF and DTU datasets, respectively.

通过引入特征匹配的稀疏几何规则化模块，本文提出了一种名为 SGCNeRF 的有效少样本神经渲染架构，通过 NeRF 迭代中几何和纹理的渐进细化，提供卓越的几何一致性结果，并在 LLFF 和 DTU 数据集上分别提高了 0.7dB 和 0.6dB 的 PSNR，优于 FreeNeRF。