SparseGS: Real-Time 360 Sparse View Synthesis using Gaussian Splatting
arxiv(2023)
摘要
The problem of novel view synthesis has grown significantly in popularity
recently with the introduction of Neural Radiance Fields (NeRFs) and other
implicit scene representation methods. A recent advance, 3D Gaussian Splatting
(3DGS), leverages an explicit representation to achieve real-time rendering
with high-quality results. However, 3DGS still requires an abundance of
training views to generate a coherent scene representation. In few shot
settings, similar to NeRF, 3DGS tends to overfit to training views, causing
background collapse and excessive floaters, especially as the number of
training views are reduced. We propose a method to enable training coherent
3DGS-based radiance fields of 360-degree scenes from sparse training views. We
integrate depth priors with generative and explicit constraints to reduce
background collapse, remove floaters, and enhance consistency from unseen
viewpoints. Experiments show that our method outperforms base 3DGS by 6.4
LPIPS and by 12.2
on the MipNeRF-360 dataset with substantially less training and inference cost.
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