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.

通过应用渐进传播策略，并利用场景的先验知识和块匹配技术，我们提出了一种名为 GaussianPro 的新方法，来指导 3D 高斯聚类的密度化，验证实验证明了我们的方法在大规模和小规模场景上的有效性。