🤖 AI Summary
To address overfitting in 3D Gaussian Splatting (3DGS) under single-view supervision—leading to artifacts in novel-view synthesis and inaccurate geometric reconstruction—this paper proposes a multi-view collaborative optimization framework. Our method introduces three key innovations: (1) a novel multi-view regularization paradigm that jointly enforces consistency across multiple views; (2) an intrinsic-cross-guided coarse-to-fine training strategy integrating multi-scale geometric and appearance priors; and (3) ray-intersection-driven cross-view densification coupled with view-difference-aware adaptive densification. While preserving real-time rendering performance, our approach significantly improves both novel-view image fidelity and 3D geometric accuracy. Extensive experiments demonstrate strong generalization across diverse scenes and mainstream 3DGS variants, outperforming existing single-view methods in both qualitative and quantitative evaluations.
📝 Abstract
Recent works in volume rendering, extit{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.