3D-HGS: 3D Half-Gaussian Splatting

Haolin Li, Jinyang Liu, Mario Sznaier, Octavia Camps

CVPR 2025 pp. 10996-11005

doi:10.1109/CVPR52734.2025.01027 /cvpr/2025/li2025cvpr-3dhgs/

Abstract

Photo-realistic image rendering from 3D scene reconstruction has advanced significantly with neural rendering techniques. Among these, 3D Gaussian Splatting (3D-GS) outperforms Neural Radiance Fields (NeRFs) in quality and speed but struggles with shape and color discontinuities. We propose 3D Half-Gaussian (3D-HGS) kernels as a plug-and-play solution to address these limitations. Our experiments show that 3D-HGS enhances existing 3D-GS methods, achieving state-of-the-art rendering quality without compromising speed. More demos and code are available at https://lihaolin88.github.io/CVPR-2025-3DHGS

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Cite

Text

Li et al. "3D-HGS: 3D Half-Gaussian Splatting." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.01027

Markdown

[Li et al. "3D-HGS: 3D Half-Gaussian Splatting." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/li2025cvpr-3dhgs/) doi:10.1109/CVPR52734.2025.01027

BibTeX

@inproceedings{li2025cvpr-3dhgs,
  title     = {{3D-HGS: 3D Half-Gaussian Splatting}},
  author    = {Li, Haolin and Liu, Jinyang and Sznaier, Mario and Camps, Octavia},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2025},
  pages     = {10996-11005},
  doi       = {10.1109/CVPR52734.2025.01027},
  url       = {https://mlanthology.org/cvpr/2025/li2025cvpr-3dhgs/}
}