GaussianPro: 3D Gaussian Splatting with Progressive Propagation
Kai Cheng Xiaoxiao Long Kaizhi Yang Yao Yao Wei Yin Yuexin Ma Wenping Wang Xuejin Chen
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.