GaussianPro: 3D Gaussian Splatting with Progressive Propagation
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.
65d81e406769b3a9c925ce20
Kai Cheng
65d81e406769b3a9c925ce21
Xiaoxiao Long
65d81e406769b3a9c925ce22
Kaizhi Yang
65d81e406769b3a9c925ce23
Yao Yao
65d81e406769b3a9c925ce24
Wei Yin
claimed_verified
2025-03-17T08:46:28.478Z
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/avatars/dc3dfc3fcd26bb7350a9db0d075c5ea0.svg
Wei Yin
user
WonderingWorld
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Yuexin Ma
65d81e406769b3a9c925ce26
Wenping Wang
65d81e406769b3a9c925ce27
Xuejin Chen
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
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.
GaussianPro improves 3D Gaussian Splatting by using a progressive propagation strategy with patch matching to better handle large-scale scenes with texture-less surfaces, enhancing rendering quality.
3D Gaussian Splatting SfM multi-view stereo GaussianPro progressive propagation patch matching PSNR Waymo dataset