Core Concepts
Exploring the use of structured hash grids to compress 3D Gaussian representations efficiently.
Abstract
The content introduces the Hash-grid Assisted Context (HAC) framework for compressing 3D Gaussian splatting models. It addresses challenges in compression due to the unorganized nature of Gaussians by leveraging mutual information from a structured hash grid. The framework achieves significant size reduction and improved fidelity compared to existing approaches. The paper includes experiments, comparisons with other methods, ablation studies, and visualization of bit allocation.
- Introduction to 3D Gaussian Splatting (3DGS) as a promising framework for view synthesis.
- Challenges in compressing sparse and unorganized Gaussians.
- Proposal of the Hash-grid Assisted Context (HAC) framework for efficient compression.
- Detailed explanation of technical components like Adaptive Quantization Module and Adaptive Offset Masking.
- Results from experiments on various datasets showcasing superior performance.
- Ablation studies highlighting the importance of different components in HAC.
- Visualization of bit allocation maps demonstrating efficient bit distribution.
Stats
PSNR=26.35dB
Size=1350MB
PSNR=27.82dB
Size=18.76MB
PSNR=26.60dB
Size=178MB
Quotes
"Extensive experiments on five datasets demonstrate the effectiveness of our HAC framework."
"Our proposed HAC has demonstrated SOTA compression performance with remarkable leading over concurrent works."