BlazeVGGT: 13x Faster VGGT at 2,000 Frames via Camera Token without Training

Key points

• We uncover a topology-first, geometry-later evolution in VGGT camera tokens: shallow-layer tokens reliably capture local view neighborhoods, whereas deeper-layer tokens progressively encode globally organized inter-view geometry.1
• Motivated by this observation, we introduce BlazeVGGT, a training-free framework for structure-aware frame-level key--value sparsification.1
• At each global-attention block, BlazeVGGT constructs a view-level similarity kernel from camera-token relations and employs greedy determinantal point process inference to select a compact set of mutually complementary views.1
• This asymmetric attention design preserves dense per-frame predictions while eliminating redundant cross-view computation, allowing our method to be directly integrated into pretrained VGGT as a plug-and-play module without parameter updates.1

Sources (1)

• [1]BlazeVGGT: 13x Faster VGGT at 2,000 Frames via Camera Token without Training
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 09:11 AM

  Feed-forward visual geometry models, exemplified by VGGT, have demonstrated strong multi-view reconstruction capabilities through dense global attention.

  We uncover a topology-first, geometry-later evolution in VGGT camera tokens: shallow-layer tokens reliably capture local view neighborhoods, whereas deeper-layer tokens progressively encode globally organized inter-view geometry.

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