Khái niệm cốt lõi
GeoGaussian enhances 3D Gaussian rendering quality by preserving accurate geometry and introducing novel densification strategies.
Tóm tắt
The article introduces GeoGaussian, a method focusing on improving the rendering quality of 3D Gaussians in non-textured regions. It proposes geometry-aware strategies for initialization and densification to enhance rendering performance. The method is evaluated on public datasets, showcasing superior results compared to state-of-the-art approaches.
- Introduction to Neural Radiance Fields (NeRF) and Gaussian Splatting.
- Challenges in maintaining scene geometry during Gaussian Splatting optimization.
- Proposal of GeoGaussian approach emphasizing geometry preservation.
- Detailed methodology including Gaussian initialization, densification, and view-dependent optimization.
- Comparison with existing methods on public datasets showcasing improved rendering quality.
- Ablation studies on sparse training views and evaluation on different datasets.
Thống kê
During the first 15K iterations, our method achieves significantly better results than 3DGS.
Our model trained on ICL-O3 (10%) achieves a PSNR of 21.42, outperforming models of 3DGS and LightGS.
Trích dẫn
"Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction."
"GeoGaussian preserves the reasonable geometry of environments compared to 3DGS."