3D-HGS: 3D Half-Gaussian Splatting*

Haolin Li, Jinyang Liu, M. Sznaier, O. Camps
2/9/2026

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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 this paper

@article{li2026dhgs,
  title  = {3D-HGS: 3D Half-Gaussian Splatting*},
  author = {Haolin Li and Jinyang Liu and M. Sznaier and O. Camps},
  year   = {2026},
  eprint = {2406.02720},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2406.02720},
  journal = {CVPR 2024 2024}
}

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