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The University of Tokyo
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NTRK achieves a 20脳 reduction in compute while surpassing the best baseline in aesthetic generation, revolutionizing reward-guided diffusion sampling.
By strategically dropping "anchor" Gaussians and their neighbors, DropAnSH-GS overcomes the limitations of naive dropout in 3D Gaussian Splatting, leading to more robust and globally informed scene representations under sparse-view conditions.
