3D-HGS: 3D Half-Gaussian Splatting

Haolin Li, Jinyang Liu, Mario Sznaier, Octavia Camps; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 10996-11005

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

Related Material

[pdf] [supp]
[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} }