MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis
Authors: name: Xiaobiao Du, name: Yida Wang, name: Xin Yu

Recent works in volume rendering, e.g. NeRF and 3D Gaussian
Splatting (3DGS), significantly advance the rendering quality and efficiency
with the help of the learned implicit neural radiance field or 3D Gaussians.
Rendering on top of an explicit representation, the vanilla 3DGS and its
variants deliver real-time efficiency by optimizing the parametric model with
single-view supervision per iteration during training which is adopted from
NeRF. Consequently, certain views are overfitted, leading to unsatisfying
appearance in novel-view synthesis and imprecise 3D geometries. To solve
aforementioned problems, we propose a new 3DGS optimization method embodying
four key novel contributions: 1) We transform the conventional single-view
training paradigm into a multi-view training strategy. With our proposed
multi-view regulation, 3D Gaussian attributes are further optimized without
overfitting certain training views. As a general solution, we improve the
overall accuracy in a variety of scenarios and different Gaussian variants. 2)
Inspired by the benefit introduced by additional views, we further propose a
cross-intrinsic guidance scheme, leading to a coarse-to-fine training procedure
concerning different resolutions. 3) Built on top of our multi-view regulated
training, we further propose a cross-ray densification strategy, densifying
more Gaussian kernels in the ray-intersect regions from a selection of views.
4) By further investigating the densification strategy, we found that the
effect of densification should be enhanced when certain views are distinct
dramatically. As a solution, we propose a novel multi-view augmented
densification strategy, where 3D Gaussians are encouraged to get densified to a
sufficient number accordingly, resulting in improved reconstruction accuracy.