We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a light-weight confidence predictor to rank tokens by uncertainty and selectively merge low-confidence ones, effectively reducing computation while maintaining spatial coverage. Compared to similarity-based merging or pruning, the confidence signal in Co-Me reliably indicates regions emphasized by the transformer, enabling substantial acceleration without degrading performance. Co-Me applies seamlessly to various multi-view and streaming visual geometric transformers, achieving speedups that scale with sequence length. When applied to VGGT and Pi3, Co-Me achieves up to 21.5x and 20.4x speedup, making visual geometric transformers practical for real-time 3D perception and reconstruction.
@article{arxiv.2511.14751,
title = {Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers},
author = {Yutian Chen and Yuheng Qiu and Ruogu Li and Ali Agha and Shayegan Omidshafiei and Jay Patrikar and Sebastian Scherer},
journal= {arXiv preprint arXiv:2511.14751},
year = {2026}
}