3D-HGS: 3D Half-Gaussian Splatting
About
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.
Haolin Li, Jinyang Liu, Mario Sznaier, Octavia Camps• 2024
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Novel View Synthesis | Mip-NeRF 360 (test) | PSNR33.5 | 199 | |
| Novel View Synthesis | Mip-NeRF360 | PSNR29.61 | 184 | |
| Novel View Synthesis | Deep Blending (test) | PSNR29.8 | 80 | |
| Novel View Synthesis | Deep Blending | PSNR29.76 | 80 | |
| Novel View Synthesis | Mip-NeRF360 (test) | PSNR26.74 | 80 | |
| Novel View Synthesis | NeRF Synthetic (test) | -- | 46 | |
| Novel View Synthesis | Tanks&Temples | PSNR24.17 | 44 | |
| Novel View Synthesis | Mip-NeRF 360 | PSNR29.772 | 37 | |
| Novel View Synthesis | Deep Blending | SSIM90.5 | 32 | |
| Novel View Synthesis | Tank & Temples (test) | PSNR25.08 | 23 |
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