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3D-HGS: 3D Half-Gaussian Splatting

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arxiv 2406.02720 v4 pith:YCH2HYQ3 submitted 2024-06-04 cs.CV cs.GR

Haolin Li , Jinyang Liu , Mario Sznaier , Octavia Camps This is my paper · ORCID
classification cs.CVcs.GR
keywords d-hgsrenderingd-gshalf-gaussianneuralqualityspeedsplatting
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deformable Radial Kernel Splatting

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A new 2D planar kernel primitive with learnable radial bases, mixed L1/L2 norms, and edge sharpening generalizes Gaussian splatting and claims better rendering quality with fewer primitives.

  2. Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives

    cs.CV 2024-11 reject novelty 6.0 of 10

    A Gaussian splatting method that fits curved paraboloid patches instead of flat disks reports better surface reconstruction, but its geodesic-distance justification is only exact for surfaces of revolution.

  3. ColorGS: High-fidelity Surgical Scene Reconstruction with Colored Gaussian Splatting

    cs.CV 2025-08 conditional novelty 3.0 of 10

    ColorGS adds spatially anchored colors and a time-independent deformation offset to 3D Gaussian Splatting, achieving 39.85 PSNR on EndoNeRF, 1.5 dB above Deform3DGS.

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