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.

翻译：三维高斯泼溅（3DGS）的出现最近彻底改变了神经渲染领域，实现了实时速度下的高质量渲染。然而，3DGS严重依赖于运动恢复结构（SfM）技术生成的初始化点云。在处理不可避免包含无纹理表面的大规模场景时，SfM技术无法在这些表面生成足够的点，从而无法为3DGS提供良好的初始化，导致3DGS面临优化困难和渲染质量低下的问题。受经典多视图立体（MVS）技术的启发，本文提出了一种新方法GaussianPro，该方法采用渐进传播策略来引导三维高斯的致密化。与3DGS中使用的简单分裂和克隆策略相比，我们的方法利用场景现有重建几何的先验信息和块匹配技术，生成具有精确位置和朝向的新高斯。在大规模和小规模场景上的实验验证了该方法的有效性，在Waymo数据集上，我们的方法显著超越3DGS，峰值信噪比（PSNR）提升了1.15dB。