The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale scenes that unavoidably contain texture-less surfaces, the SfM techniques always fail to produce enough points in these surfaces and cannot provide good initialization for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques, we propose GaussianPro, a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Compared to the simple split and clone strategies used in 3DGS, our method leverages the priors of the existing reconstructed geometries of the scene and patch matching techniques to produce new Gaussians with accurate positions and orientations. Experiments on both large-scale and small-scale scenes validate the effectiveness of our method, where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
三维高斯喷溅(3DGS)的出现最近在神经渲染领域引发了一场革命,促进了实时速度下的高质量渲染。然而,3DGS严重依赖于由结构从运动(SfM)技术产生的初始化点云。在处理不可避免包含无纹理表面的大规模场景时,SfM技术总是无法在这些表面上产生足够的点,并且不能为3DGS提供好的初始化。结果,3DGS遭受了优化困难和低质量渲染的问题。在本文中,受到经典多视图立体(MVS)技术的启发,我们提出了一种新颖的方法GaussianPro,该方法应用了一个渐进的传播策略来指导三维高斯的密集化。与3DGS中使用的简单分裂和克隆策略相比,我们的方法利用了场景已重建几何体的先验和块匹配技术,产生了具有准确位置和方向的新高斯。在大规模和小规模场景上的实验验证了我们方法的有效性,其中我们的方法在Waymo数据集上显著超越了3DGS,展示了在峰值信噪比(PSNR)方面1.15dB的改进。