title: GaussianPro: 3D Gaussian Splatting with Progressive Propagation
summary: 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.
paper_authors._id: 65d81e406769b3a9c925ce20
paper_authors.name: Kai Cheng
paper_authors._id: 65d81e406769b3a9c925ce21
paper_authors.name: Xiaoxiao Long
paper_authors._id: 65d81e406769b3a9c925ce22
paper_authors.name: Kaizhi Yang
paper_authors._id: 65d81e406769b3a9c925ce23
paper_authors.name: Yao Yao
paper_authors._id: 65d81e406769b3a9c925ce24
paper_authors.name: Wei Yin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:46:28.478Z
paper_authors._id: 654a2b1a83e7bfc4313a5cc7
paper_authors.avatarUrl: /avatars/dc3dfc3fcd26bb7350a9db0d075c5ea0.svg
paper_authors.fullname: Wei Yin
paper_authors.type: user
paper_authors.user: WonderingWorld
paper_authors._id: 65d81e406769b3a9c925ce25
paper_authors.name: Yuexin Ma
paper_authors._id: 65d81e406769b3a9c925ce26
paper_authors.name: Wenping Wang
paper_authors._id: 65d81e406769b3a9c925ce27
paper_authors.name: Xuejin Chen
paper_title: GaussianPro: 3D Gaussian Splatting with Progressive Propagation
paper_summary: 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.
paper_ai_keywords: 3D Gaussian Splatting (3DGS) neural rendering real-time speed Structure-from-Motion (SfM) texture-less surfaces optimization GaussianPro progressive propagation strategy 3D Gaussians split and clone strategies priors reconstructed geometries patch matching techniques PSNR Waymo dataset