Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction
Read the original on arXiv Machine Learning →Zebra3D introduces a layerwise sparse‑linear hybrid attention framework that enhances the efficiency of pretrained 3D foundation models without requiring training from scratch. By assigning linear‑centered attention to tolerant early stages and tile‑sparse attention to sensitive middle‑to‑late stages, it preserves context through landmark summaries and enables training‑free conversion via closed‑form affine fitting. Across multiple backbones, Zebra3D achieves strong reconstruction and camera‑pose accuracy with high attention sparsity, delivering up to 7.1× speedups at 1,024 views and maintaining performance close to dense counterparts after optional post‑conversion training.
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