FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting
Zehao Zhu Zhiwen Fan Yifan Jiang Zhangyang Wang
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/.