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.
新视图合成是计算机视觉中的一个重要问题，广泛应用于三维重建、混合现实和机器人领域。近年来，三维高斯点云（3D Gaussian Splatting, 3DGS）成为该任务的首选方法，能够实时提供高质量的新视图。然而，3DGS 模型的训练时间较长，对于包含 200 个视图的场景，通常需要 30 分钟的优化时间。 针对这一问题，我们的目标是在减少训练步骤的同时保持高渲染质量。具体来说，我们结合了位置误差和外观误差的引导，来实现更高效的高斯密化过程。为平衡添加新高斯和优化旧高斯的速率，我们设计了一种 收敛感知预算控制机制。此外，为了提高密化过程的可靠性，我们优先从访问频率较高的区域选择添加新高斯。 通过这些设计，我们将高斯优化步骤减少到原方法的三分之一，同时在新视图渲染质量上保持相当甚至更好的表现。为了进一步加速 4K 分辨率图像的拟合，我们引入了一种基于膨胀的渲染技术。我们的方法 Turbo-GS 不仅加速了典型场景的优化，还能够良好扩展至高分辨率（4K）场景。 大量实验表明，Turbo-GS 相较于其他方法在优化速度上显著更快，同时保留了高质量的渲染效果。