multi-view 3D reconstruction

Good Token Hunting: Token Selection Framework for Visual Geometry Transformers

This paper introduces a two-stage token selection framework to address the quadratic computational scaling of global attention in visual geometry transformers used for multi-view 3D reconstruction. The approach combines diversity-based inter-frame selection (frame-level) with entropy-guided intra-frame sparsification (token-level within frames). Experiments demonstrate over 85% acceleration for 500-image scenes while maintaining or improving baseline reconstruction quality, offering a favorable speed-accuracy trade-off.