SparseSCIGaussian: Sparse snapshot compressed images 3D Gaussian splatting

Abstract

In this paper, we propose SparseSCIGaussian, a novel method for achieving high-quality novel view synthesis under sparse input conditions. Previous methods often rely heavily on depth or neural priors, which can lead to generalization challenges and significant quality degradation on complex datasets. These limitations arise primarily from the insufficient scene information available in sparse regular images. To overcome these issues, our approach utilizes images captured through Snapshot Compressive Imaging (SCI) as input. SCI-captured images inherently encode richer scene information compared to regular images, thereby substantially improving the quality of novel view synthesis under sparse input conditions. Moreover, SCI images can be conveniently captured using a software-implemented encoder, making them as accessible as traditional images. Experimental results demonstrate that our method improves 2.65 dB (13.04 %) in PSNR compared to previous methods, and further exhibits the inherent advantages of using SCI images for sparse input novel view synthesis.