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Feed-forward 3D foundation models reconstruct scenes from unposed images, but the quadratic cost of global attention limits their scalability to large view sets. We present Zebra3D, a layerwise sparse-linear hybrid attention framework that improves the efficiency of pretrained 3D foundation models without training from...
Instella-MoE is introduced, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active parameters per token trained entirely from scratch on AMD Instinct MI300X and MI325X GPUs, establishing a strong, fully open foundation for efficient, high-performing MoE models and r...
A rendering-aware Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries is introduced, positioning Bayesian 3DGS as a practical probabilistic scene representation for decision-facing tasks such as active view s...
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