Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields (2412.13547v1)

Abstract: Novel-view synthesis is an important problem in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent methods like 3D Gaussian Splatting (3DGS) have become the preferred method for this task, providing high-quality novel views in real time. However, the training time of a 3DGS model is slow, often taking 30 minutes for a scene with 200 views. In contrast, our goal is to reduce the optimization time by training for fewer steps while maintaining high rendering quality. Specifically, we combine the guidance from both the position error and the appearance error to achieve a more effective densification. To balance the rate between adding new Gaussians and fitting old Gaussians, we develop a convergence-aware budget control mechanism. Moreover, to make the densification process more reliable, we selectively add new Gaussians from mostly visited regions. With these designs, we reduce the Gaussian optimization steps to one-third of the previous approach while achieving a comparable or even better novel view rendering quality. To further facilitate the rapid fitting of 4K resolution images, we introduce a dilation-based rendering technique. Our method, Turbo-GS, speeds up optimization for typical scenes and scales well to high-resolution (4K) scenarios on standard datasets. Through extensive experiments, we show that our method is significantly faster in optimization than other methods while retaining quality. Project page: https://ivl.cs.brown.edu/research/turbo-gs.

• The paper introduces Turbo-GS, a method that significantly accelerates 3D Gaussian Splatting training for high-quality radiance fields, achieving up to 5x speedup over previous methods without sacrificing rendering quality.
• Turbo-GS enhances densification by integrating both position and appearance errors, improving scene convergence and structural retention, especially in texture-less areas.
• A convergence-aware budget control mechanism dynamically adjusts the densification rate based on observed convergence trends, managing the addition of new Gaussian primitives efficiently.

Overview of "Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields"

The paper "Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields" presents an innovative approach to optimizing 3D Gaussian Splatting (3DGS) for rapid training of radiance fields, aimed at applications in novel-view synthesis, 3D reconstruction, and mixed reality. This work proposes Turbo-GS, which seeks to substantially reduce the optimization steps required for high-quality rendering while maintaining, or even enhancing, rendering quality compared to existing 3DGS methods.

Key Contributions

The authors introduce several core strategies to accelerate 3DGS:

1. Efficient Densification Strategy:
   • The Turbo-GS method enhances the densification process by integrating both position and appearance errors, rather than relying solely on positional gradients. This dual-gradient approach addresses limitations in texture-less regions by treating areas with insufficient positional data, utilizing color-based gradients for better scene convergence and retention of structural information.
2. Convergence-aware Budget Control:
   • A novel, convergence-aware budget mechanism is developed, allowing effective control over the number of Gaussian primitives added during optimization. The authors build a power-function-based adaptive schedule, dynamically adjusting the densification rate based on the observed deviation in convergence trends.
3. Selective Densification:
   • An adaptive thresholding approach is employed, ensuring that new Gaussians are densified only in regions with frequent visibility, thereby refining the selection process and minimizing unnecessary complexity in less frequently observed regions.
4. High-Resolution Rendering via Dilated Rendering:
   • Turbo-GS introduces a dilated rendering pipeline that uses a chessboard sampling pattern. This strategy significantly reduces the computational workload associated with high-resolution images, maintaining sufficient pixel information while avoiding redundant pixel supervision.

Experimental Evaluation

The paper reports extensive experimental evaluations on multiple datasets, demonstrating clear advantages over existing methods. Turbo-GS achieves notable improvements in training times—up to five times faster than the original 3DGS and significantly outperforming other recent advancements like Taming 3DGS and Scaffold-GS. Quality metrics such as PSNR, SSIM, and LPIPS indicate that Turbo-GS provides comparable or superior rendering quality across tested scenes.

Implications and Future Work

The accelerated fitting achieved by Turbo-GS significantly broadens the practical applicability of 3DGS approaches in real-time applications where rapid scene adaptation is crucial, such as augmented reality and robotics. The methodological advancements present in Turbo-GS also provide a new optimization framework that could be adapted for other complex modeling tasks in computer vision and graphics.

The paper suggests that future work might explore integrating Turbo-GS with large-scale foundation models to potentially leverage learned prior knowledge for further acceleration and robustness against varying scene complexities. As large AI models continue to evolve, such synergistic approaches could redefine the efficiency and capability of novel-view synthesis systems.

In conclusion, Turbo-GS advances the field of real-time radiance field rendering by introducing methodological innovations that significantly cut the time overhead without compromising quality, setting a new benchmark for future developments in this domain.