GeoRGS: Geometric Regularization for Real-Time Novel View Synthesis From Sparse Inputs

Zhaoliang Liu, Jinhe Su, Guorong Cai, Yidong Chen, Binghui Zeng, Zongyue Wang

Abstract: When the number of available training views is limited, NeRF and 3DGS will soon overfit the optimization and learn the wrong scene geometry. For this challenge, a common solution is to provide depth prior as supervision to correct scene geometry. In this work, we present Geometric Regularized 3D Gaussian Splatting (GeoRGS), a priors-independent method for improving novel view synthesis from sparse inputs. We analyze the problems of the density control strategy in 3DGS with sparse inputs, and find that correcting the erroneous Gaussian growth trend at the beginning of training is effective in mitigating overfitting. Based on this analysis, we propose two geometric regularization methods that do not require prior information. One is based on selecting seed patches of 3D Gaussian from the scene, which guides growth to form correct scene geometry, while the other focuses on regularizing depth similarity between object surfaces and edges. GeoRGS achieves state-of-the-art performance in novel view synthesis from sparse input on LLFF, Blender, RealEstate10K and MipNeRF360 datasets, while also demonstrating significantly faster training speeds and rendering efficiency compared to other baselines.