Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting (2403.09413v2)

Abstract: 3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When the quality of the initial point cloud deteriorates, such as in the presence of noise or when using randomly initialized point cloud, 3DGS often undergoes large performance drops. To address this limitation, we propose a novel optimization strategy dubbed RAIN-GS (Relaing Accurate Initialization Constraint for 3D Gaussian Splatting). Our approach is based on an in-depth analysis of the original 3DGS optimization scheme and the analysis of the SfM initialization in the frequency domain. Leveraging simple modifications based on our analyses, RAIN-GS successfully trains 3D Gaussians from sub-optimal point cloud (e.g., randomly initialized point cloud), effectively relaxing the need for accurate initialization. We demonstrate the efficacy of our strategy through quantitative and qualitative comparisons on multiple datasets, where RAIN-GS trained with random point cloud achieves performance on-par with or even better than 3DGS trained with accurate SfM point cloud. Our project page and code can be found at https://ku-cvlab.github.io/RAIN-GS.

• The paper presents RAIN-GS, an innovative strategy that relaxes the need for accurate SfM initialization by using sparse-large-variance random initialization and progressive low-pass filtering.
• The methodology strategically shifts from learning coarse low-frequency components to refining fine details, resulting in improved convergence and rendering quality.
• Experimental validation shows significant performance gains, broadening the applicability of 3D Gaussian Splatting to scenarios with sparse or inadequate initialization.

Novel Strategy for Training 3D Gaussian Splatting Without Accurate Initialization

Introduction

3D Gaussian Splatting (3DGS) has emerged as a promising alternative for real-time novel view synthesis and 3D reconstruction, offering both high-quality results and real-time rendering capabilities. Nonetheless, its heavy dependency on accurate initialization derived from Structure-from-Motion (SfM) methods limits its applicability, especially in scenarios where SfM techniques fail. This paper introduces a novel optimization strategy, RAIN-GS (Relaxing Accurate INitialization Constraint for 3D Gaussian Splatting), aimed at successfully training 3D Gaussians from randomly initialized point clouds, thus addressing the constraints tied to the necessity of accurately initialized point clouds from SfM.

Analysis of Initialization Methods

The paper begins by analyzing the significance of initialization in 3DGS, particularly focusing on the difference between SfM-derived point clouds and randomly-initialized ones. It details how SfM initialization offers a coarse approximation of the scene, thereby providing a solid foundation for subsequent refinements, in contrast to random initialization which often fails to capture such essential low-frequency information. Through the lens of frequency domain analysis and a simplified 1D regression task, the paper underscores the importance of initially learning the coarse distribution to guide the optimization process effectively.

RAIN-GS Strategy

Building on this analysis, the research proposes RAIN-GS, an optimization strategy that blends two key components:

1. Sparse-large-variance (SLV) random initialization - introducing a novel initialization method that starts with sparse 3D Gaussians endowed with large variance, encouraging the model to initially focus on learning the coarse, low-frequency components of the distribution.
2. Progressive Gaussian low-pass filtering - a dynamic tactic in the rendering process that gradually sharpens the focus from coarse to fine details by modifying the extent of Gaussian low-pass filtering based on the iterative progress of training.

Experimental Validation

Empirical validations on standard datasets substantiate the efficacy of RAIN-GS. Comparisons demonstrate a marked improvement in performance across various metrics, affirming the strategy's capacity to guide the learning process towards a more robust understanding of the scene, even in the absence of precise initialization. Furthermore, the paper explores the potential extension of RAIN-GS to training 3DGS under sparse view settings, showcasing its ability to compensate for the limitations of SfM in such scenarios.

Theoretical and Practical Implications

Theoretically, this paper elucidates the critical role of initialization in the convergence of 3D Gaussian models and the significance of prioritizing low-frequency component learning for robust optimization. Practically, by relaxing the stringent requirement for accurately initialized point clouds, RAIN-GS broadens the applicability of 3DGS to scenarios where acquiring such initialization is challenging or impossible. This breakthrough holds promising prospects for advancing real-time novel view synthesis and 3D reconstruction technologies.

Future Directions

The paper closes by acknowledging limitations and envisioning future work. One noted limitation is the potential failure in detecting the need for densification to capture high-frequency details in certain scenarios, suggesting that further refinements in the strategy could enhance its efficacy. Future research directions include exploring additional supervision methods to overcome identified limitations and extending the strategy to a wider range of applications within the 3D reconstruction and novel view synthesis fields.