  3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D
Gaussians to represent a scene. With sparse training views, 3DGS easily suffers
from overfitting, negatively impacting rendering. This paper introduces a new
co-regularization perspective for improving sparse-view 3DGS. When training two
3D Gaussian radiance fields, we observe that the two radiance fields exhibit
point disagreement and rendering disagreement that can unsupervisedly predict
reconstruction quality, stemming from the randomness of densification
implementation. We further quantify the two disagreements and demonstrate the
negative correlation between them and accurate reconstruction, which allows us
to identify inaccurate reconstruction without accessing ground-truth
information. Based on the study, we propose CoR-GS, which identifies and
suppresses inaccurate reconstruction based on the two disagreements: (1)
Co-pruning considers Gaussians that exhibit high point disagreement in
inaccurate positions and prunes them. (2) Pseudo-view co-regularization
considers pixels that exhibit high rendering disagreement are inaccurate and
suppress the disagreement. Results on LLFF, Mip-NeRF360, DTU, and Blender
demonstrate that CoR-GS effectively regularizes the scene geometry,
reconstructs the compact representations, and achieves state-of-the-art novel
view synthesis quality under sparse training views.
