Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification (2411.12788v1)

Abstract: In this study, we explore the essential challenge of fast scene optimization for Gaussian Splatting. Through a thorough analysis of the geometry modeling process, we reveal that dense point clouds can be effectively reconstructed early in optimization through Gaussian representations. This insight leads to our approach of aggressive Gaussian densification, which provides a more efficient alternative to conventional progressive densification methods. By significantly increasing the number of critical Gaussians, we enhance the model capacity to capture dense scene geometry at the early stage of optimization. This strategy is seamlessly integrated into the Mini-Splatting densification and simplification framework, enabling rapid convergence without compromising quality. Additionally, we introduce visibility culling within Gaussian Splatting, leveraging per-view Gaussian importance as precomputed visibility to accelerate the optimization process. Our Mini-Splatting2 achieves a balanced trade-off among optimization time, the number of Gaussians, and rendering quality, establishing a strong baseline for future Gaussian-Splatting-based works. Our work sets the stage for more efficient, high-quality 3D scene modeling in real-world applications, and the code will be made available no matter acceptance.

• The paper introduces aggressive Gaussian densification for rapid 360 scene reconstruction, significantly reducing optimization time.
• It utilizes a novel visibility culling technique to focus on essential Gaussians, ensuring efficient computation.
• Experimental results demonstrate up to 7.6x acceleration while maintaining high rendering quality on standard metrics.

Mini-Splatting2: Efficient 360-Degree Scene Optimization with Gaussian Densification

The paper, "Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification," presents an innovative approach to optimizing 3D scene models using 3D Gaussian Splatting (3DGS). The authors, Guangchi Fang and Bing Wang, from The Hong Kong Polytechnic University, introduce a novel framework for rapidly reconstructing high-quality 3D models by leveraging aggressive Gaussian densification combined with a unique visibility culling technique. This work addresses critical challenges in scene reconstruction, offering a substantial improvement in optimization time and computational efficiency without sacrificing rendering quality.

Key Contributions

1. Aggressive Gaussian Densification: The primary innovation of Mini-Splatting2 lies in its aggressive approach to Gaussian densification. This strategy sharply increases the number of critical Gaussians early in the optimization process, enabling dense scene geometry reconstruction at a fraction of the time taken by previous methods. This is achieved through a novel depth reinitialization and cloning technique that capitalizes on point cloud reconstruction capabilities inherent to Gaussian centers.
2. Visibility Gaussian Culling: To complement the densification strategy, the paper introduces visibility culling. By calculating per-view Gaussian importance and using it as precomputed visibility data, the method restricts optimization to only significant Gaussians, cutting down unnecessary computation. This not only speeds up the process but also ensures resource-efficient rendering.
3. Balanced Trade-offs: The proposed Mini-Splatting2 framework effectively balances the number of Gaussians used, optimization time, and rendering quality. It seamlessly integrates with the Mini-Splatting framework, employing simplification techniques to reduce the Gaussian count while maintaining visual fidelity of the rendered scenes.

Experimental Results

Extensive experiments conducted on multiple datasets demonstrate the superiority of Mini-Splatting2 over traditional methods. The aggressive densification and visibility culling techniques result in a 7.6$\times$ acceleration compared to the original 3DGS and a 2.8$\times$ speed-up relative to the improved implementation of 3DGS (3DGS-accel). Notably, this efficiency gain does not come at the cost of rendering quality, as Mini-Splatting2 matches or exceeds the performance of these methods on standard metrics such as SSIM, PSNR, and LPIPS.

Implications and Future Directions

The implications of this work are significant for real-time applications requiring rapid 3D scene modeling, such as in augmented reality, robotics, and immersive graphics. The methodology introduced by Mini-Splatting2 could redefine processing pipelines in industries where time-efficient 3D scene reconstructions are necessary. Moreover, the code accompanying the research will be made publicly available, providing a robust baseline for future advancements in Gaussian Splatting and related projects.

Looking forward, the integration of additional geometric features and machine learning-based predictions to further refine point cloud reconstructions and enhance depth estimation could be promising directions. Furthermore, exploring how these techniques could dovetail with multi-view stereo methods or be adapted to environments with limited computational resources would be worthwhile to extend Mini-Splatting2's applicability.

In conclusion, Mini-Splatting2 represents a significant advancement in the efficiency of scene reconstruction using Gaussian Splatting, providing a robust framework that paves the way for future innovations in the field.