VGGT-Prime: Compute-Adaptive Mixture-of-Heads for Efficient Visual Geometry Transformers

arXiv:2609.23733 · cs.CV · Submitted 2026-09-20 · Read on arXiv

cs.CV

Submitted: 2026-09-20

Updated: 2026-09-20

Terminology

• Transformer — 26× in this paper · explained in 2 episodes
• Redundancy — 20× in this paper · explained in 2 episodes
• AUC — 15× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• Pruning — 8× in this paper · explained in 4 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Attention Mechanism — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Attention Entropy — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Visual Geometry Grounded Transformer — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• In European Conference on Computer Vision — 1× in this paper
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Relative Translation Accuracy — 1× in this paper
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention
• Attention-based Deep Multiple Instance Learning
• MoH: Multi-Head Attention as Mixture-of-Head Attention
• Depth Anything 3: Recovering the Visual Space from Any Views
• Block-Sparse Global Attention for Efficient Multi-View Geometry Transformers

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