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About this paper
Binxiao Huang, Zhengwu Liu, Ngai Wong
Researchers from The University of Hong Kong introduced a method to accelerate 3D Gaussian Splatting (3DGS) training by decomposing densification operations and employing a global-to-local strategy with energy-guided multi-resolution training. This approach reduces training time by approximately 2x and Gaussian primitives by 40%, while improving PSNR by 0.31 dB and SSIM by 0.004 compared to a baseline.
Key insights
- Split-dominated Gaussian primitives exhibit approximately twenty times greater displacement from their initial positions compared to clone-dominated ones, indicating distinct roles in spatial diffusion.
- Approximately 80% of new Gaussian primitives originate from cloning operations, particularly in early training stages, suggesting that early, unoptimized cloning leads to redundancy.
- Training with full-resolution images too early can result in premature local optimization and inefficient spatial expansion of Gaussians, benefiting from a gradual increase in resolution.
Summary by alphaXiv.
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