Core Concepts
Exploring the relationship between unorganized Gaussians and structured hash grids for compact 3DGS representation.
Abstract
The content discusses a novel approach, HAC, for compressing 3D Gaussian splatting models. It introduces a structured hash grid to exploit mutual information between unorganized anchors and the grid for context modeling. The framework achieves significant size reduction while maintaining fidelity. The article covers experiments, comparisons with existing methods, ablation studies, bit allocation visualization, and training details.
Structure:
Introduction to 3D scene representations.
Emergence of 3D Gaussian Splatting (3DGS).
Challenges in compressing 3D Gaussians.
Proposed Hash-grid Assisted Context (HAC) framework.
Abstract summarizing the key contributions.
Related work on Neural Radiance Field compression.
Methods section detailing bridging anchors and hash grids.
Experiment results on various datasets.
Ablation study on different components of HAC.
Visualization of bit allocation maps.
Training and execution time analysis.
Stats
PSNR=26.35dB
Size=1350MB
PSNR=27.82dB
Size=18.76MB
PSNR=26.60dB
Size=178MB
Quotes
"We are the first to model contexts for 3DGS compression."
"Our core idea is to jointly learn structured compact hash grid."
"Our work has successfully mitigated the major challenge of large storage requirement."