Turbo-GS: 加速3D高斯拟合以实现高质量辐射场
Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
AI总结：
本文提出Turbo-GS方法，通过减少计算开销和提升学习效率加速3D高斯拟合，实现4K分辨率快速拟合并保持高质量视图渲染。
摘要：
新颖视角合成在计算机视觉中扮演关键角色，应用于3D重建、混合现实和机器人领域。最近的3D高斯散射（3DGS）方法成为最先进的解决方案，提供实时高质量新颖视图合成。然而，训练3DGS模型仍较慢，特别是对于高分辨率图像，往往需要数小时才能拟合200个视图。在本文中，我们旨在通过减少计算开销和提升学习效率来加速拟合过程。具体来说，我们引入了一种扩张渲染技术，仅渲染像素子集而非完整图像，显著降低计算成本。为提升学习效率，我们开发了一种收敛意识预算控制机制，平衡新高斯的添加与现有高斯的优化。此外，为提高密集化效率并防止梯度消失，我们结合位置和外观误差以提高密集化的有效性。通过这些改进，我们实现了快速4K分辨率拟合，同时保持或甚至提升新颖视图渲染质量。大量实验表明，我们的方法在优化速度上显著优于现有方法，同时保持高渲染保真度。
英文摘要：
Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches, such as 3D Gaussian Splatting (3DGS), have emerged as state-of-the-art solutions, offering high-quality novel view synthesis in real time. However, training 3DGS models remains slow, particularly for high-resolution images, often requiring hours to fit a scene with 200 views. In this work, we aim to accelerate the fitting process by reducing computational overhead and improving learning efficiency. Specifically, we introduce a dilated rendering technique that renders only a subset of pixels instead of the full image, significantly reducing computational costs. To enhance learning efficiency, we develop a convergence-aware budget control mechanism that balances the addition of new Gaussians with the optimization of existing ones. Additionally, to improve densification efficiency and prevent gradient vanishing, we incorporate both positional and appearance errors to improve the effectiveness of densification. With these improvements, we achieve fast 4K-resolution fitting while maintaining, or even improving, novel view rendering quality. Extensive experiments demonstrate that our method achieves significantly faster optimization than existing approaches while preserving high rendering fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
