3D-HGS: 3D Half-Gaussian Splatting

Haolin Li; Jinyang Liu; Mario Sznaier; Octavia Camps

CVPR 2025poster1 citations

3D-HGS: 3D Half-Gaussian Splatting

Haolin Li, Jinyang Liu, Mario Sznaier, Octavia Camps

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

BibTeX

@InProceedings{Li_2025_CVPR,
    author    = {Li, Haolin and Liu, Jinyang and Sznaier, Mario and Camps, Octavia},
    title     = {3D-HGS: 3D Half-Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10996-11005}
}