MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis (2410.02103v1)

Abstract: Recent works in volume rendering, \textit{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.

• The paper introduces a multi-view regulated training strategy that mitigates overfitting in Gaussian splatting for novel view synthesis.
• It employs a coarse-to-fine cross-intrinsic guidance and cross-ray densification to refine detail preservation across multiple view resolutions.
• Empirical evaluations demonstrate a roughly 1 dB PSNR gain, indicating enhanced rendering accuracy for complex scenes.

Analysis of Multi-view-regulated Gaussian Splatting for Novel View Synthesis

The paper "MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis" proposes an advanced methodology aimed at enhancing novel view synthesis (NVS) using Gaussian-based explicit representations. The authors identify significant issues associated with overfitting in the current single-view supervised training paradigms utilized in Gaussian Splatting (3DGS) frameworks. To address these, they introduce a series of novel strategies for optimizing 3DGS, which potentially improve both the accuracy and robustness of synthesized views.

Core Contributions

The paper's methodology encompasses four principal innovations:

1. Multi-view Regulation: A cornerstone of the paper is transforming single-view training into a multi-view regulated strategy. This shift enables the optimization of 3D Gaussian attributes across multiple views simultaneously, thereby mitigating the overfitting problem and increasing overall rendering accuracy.
2. Cross-intrinsic Guidance: Building on the advantages of multi-view supervision, the authors introduce a coarse-to-fine training approach across different resolutions. This strategy allows for capturing broader information at lower resolutions, which is then refined at higher resolutions, facilitating better detail preservation.
3. Cross-ray Densification: This method densifies Gaussian kernels in regions where multiple rays intersect, leveraging a selection of views. This densification intends to enhance reconstruction accuracy by focusing on areas that contribute significantly across views.
4. Multi-view Augmented Densification: Realizing that densification effects increase when views differ substantially, an augmented strategy is proposed. This method aids in dynamically managing the number of Gaussian primitives to fit diverse perspectives robustly.

Quantitative Insights

Empirical evaluations show that the proposed MVGS framework consistently improves the NVS performance across a wide range of challenging scenarios. The enhancement is quantitatively reflected by an approximately 1 dB increase in PSNR across different tasks, including reflective object reconstruction, 4D dynamic scenes, and large-scale scene reconstructions.

Implications and Future Directions

The implications of this research are twofold:

• Practical Implications: The methods proposed can be seamlessly integrated into existing Gaussian-based frameworks with minimal code changes, suggesting broad applicability in contexts such as multimedia generation and virtual reality.
• Theoretical Implications: The introduction of multi-view regulated learning and cross-intrinsic guidance sets a new precedent in the optimization of explicit rendering techniques, offering a potentially significant improvement over conventional single-view methods.

Moving forward, the exploration of integrating these methods with more advanced neural rendering techniques could be a promising avenue. Additionally, the adaptation to even more dynamic and complex scenes could further validate the robustness of the approach.

Overall, this paper not only advances the state-of-the-art in Gaussian-based rendering but also provides a framework that could influence future research directions in novel view synthesis and 3D scene reconstruction.