GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting
📰 ArXiv cs.AI
Learn how to optimize 3D Gaussian Splatting using automatic joint structured pruning and quantization with GETA-3DGS, improving performance for mobile and immersive platforms
Action Steps
- Apply GETA-3DGS to your 3DGS model to automatically prune and quantize the Gaussian splats
- Use the pruned and quantized model to reduce memory usage and improve rendering speed
- Evaluate the performance of the optimized model using metrics such as PSNR and SSIM
- Compare the results with existing compression methods like HAC++ and FlexGaussian
- Fine-tune the GETA-3DGS hyperparameters to achieve the best trade-off between compression ratio and rendering quality
Who Needs to Know This
Computer vision engineers and researchers working on 3D representation and novel-view synthesis can benefit from this technique to improve the efficiency of their models
Key Insight
💡 Automatic joint structured pruning and quantization can significantly improve the efficiency of 3D Gaussian Splatting models
Share This
🔍 Improve 3D Gaussian Splatting performance with GETA-3DGS! 🚀 Automatic joint pruning and quantization for faster rendering and lower memory usage 📊
Key Takeaways
Learn how to optimize 3D Gaussian Splatting using automatic joint structured pruning and quantization with GETA-3DGS, improving performance for mobile and immersive platforms
Full Article
Title: GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting
Abstract:
arXiv:2605.02086v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) is a state-of-the-art representation for real-time photorealistic novel-view synthesis, yet a single high-fidelity scene typically occupies hundreds of megabytes to several gigabytes, exceeding the budgets of mobile, immersive, and volumetric video platforms. Existing 3DGS compression methods (e.g., HAC++, FlexGaussian, LP-3DGS) treat pruning, quantization, and entropy coding as separate stages and rely on hand-tuned
Abstract:
arXiv:2605.02086v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) is a state-of-the-art representation for real-time photorealistic novel-view synthesis, yet a single high-fidelity scene typically occupies hundreds of megabytes to several gigabytes, exceeding the budgets of mobile, immersive, and volumetric video platforms. Existing 3DGS compression methods (e.g., HAC++, FlexGaussian, LP-3DGS) treat pruning, quantization, and entropy coding as separate stages and rely on hand-tuned
DeepCamp AI