GS-NFS accelerates dynamic 3DGS encoding and decoding by 1-2 orders of magnitude on GPU while maintaining competitive compression ratios and rendering quality.
Towards 3d human pose construction using wifi
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
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citation-polarity summary
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2026 2verdicts
UNVERDICTED 2roles
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baseline 1representative citing papers
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
citing papers explorer
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GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
GS-NFS accelerates dynamic 3DGS encoding and decoding by 1-2 orders of magnitude on GPU while maintaining competitive compression ratios and rendering quality.
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.