Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting

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 study 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 study 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.