Pith. sign in

REVIEW 1 cited by

3D-HGS: 3D Half-Gaussian Splatting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
classification cs.CVcs.GR
keywords d-hgsrenderingd-gshalf-gaussianneuralqualityspeedsplatting
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. 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.

Pith tools