Novel view synthesis from limited observations remains an important and persistent task. However, high efficiency in existing NeRF-based few-shot view synthesis is often compromised to obtain an accurate 3D representation. To address this challenge, we propose a few-shot view synthesis framework based on 3D Gaussian Splatting that enables real-time and photo-realistic view synthesis with as few as three training views. The proposed method, dubbed FSGS, handles the extremely sparse initialized SfM points with a thoughtfully designed Gaussian Unpooling process. Our method iteratively distributes new Gaussians around the most representative locations, subsequently infilling local details in vacant areas. We also integrate a large-scale pre-trained monocular depth estimator within the Gaussians optimization process, leveraging online augmented views to guide the geometric optimization towards an optimal solution. Starting from sparse points observed from limited input viewpoints, our FSGS can accurately grow into unseen regions, comprehensively covering the scene and boosting the rendering quality of novel views. Overall, FSGS achieves state-of-the-art performance in both accuracy and rendering efficiency across diverse datasets, including LLFF, Mip-NeRF360, and Blender. Project website: https://zehaozhu.github.io/FSGS/.

翻译：从有限观测中合成新视角仍是一项重要且持续存在的任务。然而，现有基于NeRF的少样本视图合成方法往往需要在效率上做出妥协，以获得精确的三维表示。为解决这一挑战，我们提出了一种基于三维高斯点绘的少样本视图合成框架，仅需三张训练视图即可实现实时且逼真的视图合成。该方法名为FSGS，通过精心设计的高斯解池过程处理极度稀疏的初始化SfM点。我们的方法迭代地在最具代表性的位置周围分布新的高斯点，随后填充空白区域的局部细节。我们还在高斯点优化过程中集成大规模预训练的单目深度估计器，利用在线增强视图引导几何优化趋向最优解。从有限输入视角观测到的稀疏点出发，FSGS能精确扩展至未观测区域，全面覆盖场景并提升新视角的渲染质量。总体而言，FSGS在LLFF、Mip-NeRF360和Blender等多个数据集上的准确性与渲染效率均达到最优水平。项目网站：https://zehaozhu.github.io/FSGS/。