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MEGS$^{2}$: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruning
arXiv – CS AI|Jiarui Chen, Yikeng Chen, Yingshuang Zou, Ye Huang, Peng Wang, Yuan Liu, Yujing Sun, Wenping Wang||3 views
🤖AI Summary
Researchers introduce MEGS², a new memory-efficient framework for 3D Gaussian Splatting that reduces memory consumption by 50% for static rendering and 40% for real-time rendering. The breakthrough enables 3D rendering on edge devices by replacing memory-intensive spherical harmonics with lightweight spherical Gaussian lobes and implementing unified pruning optimization.
Key Takeaways
- →MEGS² achieves 50% static VRAM reduction and 40% rendering VRAM reduction compared to existing 3D Gaussian Splatting methods.
- →The framework replaces memory-intensive spherical harmonics with lightweight spherical Gaussian lobes for color representation.
- →A unified soft pruning framework optimizes both primitive number and lobe number as a single constrained optimization problem.
- →The innovation makes high-quality 3D rendering accessible on edge devices with limited memory.
- →Rendering quality remains comparable to existing methods despite significant memory reductions.
#3d-rendering#memory-optimization#gaussian-splatting#edge-computing#computer-vision#ai-research#vram-reduction#novel-view-synthesis
Read Original →via arXiv – CS AI
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