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 the reconstruction quality. This paper
introduces a new co-regularization perspective for improving sparse-view 3DGS.
When training two 3D Gaussian radiance fields with the same sparse views of a
scene, we observe that the two radiance fields exhibit \textit{point
disagreement} and \textit{rendering disagreement} that can unsupervisedly
predict reconstruction quality, stemming from the sampling implementation in
densification. We further quantify the point disagreement and rendering
disagreement by evaluating the registration between Gaussians' point
representations and calculating differences in their rendered pixels. The
empirical study demonstrates the negative correlation between the two
disagreements 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: (\romannumeral1) Co-pruning
considers Gaussians that exhibit high point disagreement in inaccurate
positions and prunes them. (\romannumeral2) Pseudo-view co-regularization
considers pixels that exhibit high rendering disagreement are inaccurately
rendered 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.

该论文介绍了一种新的协同正则化方法来改善稀疏视图下 3D 高斯光场的重建质量，通过评估高斯点表示之间的注册以及渲染像素之间的差异来量化点和渲染不一致性，进一步研究表明这两种不一致性与精确的重建之间存在负相关，基于这些研究结果，提出了一种名为 CoR-GS 的方法来鉴别和抑制不准确的重建，并在多个数据集上验证了该方法的有效性。