This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed Distance Function (ASDF), which anneals the surface in a coarse-to-fine manner to accelerate convergence speed. We observe that the Eikonal loss - which is a widely known geometric regularization - requires dense training signal to shape different level-sets of SDF, leading to low-fidelity results under few-shot training. In contrast, the proposed surface regularization successfully reconstructs scenes and produce high-fidelity geometry with stable training. Our method is further accelerated by utilizing grid representation and monocular geometric priors. Finally, the proposed approach is up to 45 times faster than existing few-shot novel view synthesis methods, and it produces comparable results in the ScanNet dataset and NeRF-Real dataset.

该论文提出一种用于在少样本环境下合成新视角的算法。其核心概念是开发一种稳定的表面正则化技术，称为退火有符号距离函数(ASDF)，通过粗到细的表面退火方法加速收敛速度。与现有少样本训练方法相比，该方法能够成功重构场景并生成高保真几何图形，并且在ScanNet数据集和NeRF-Real数据集上能够以多达45倍的速度提供可比较的结果。

快速Few-Shot NeRF的稳定表面正则化