FVO: Fast Visual Odometry with Transformers

ArXi:2510.03348v3 Announce Type: replace Hybrid pipelines that combine deep learning with classical optimization have established themselves as the dominant approach to visual odometry (VO). By integrating neural network predictions with bundle adjustment, these models estimate camera trajectories with high accuracy. Still, hybrid VO methods fall short of the speed and capabilities of pure end-to-end approaches. Current hybrid frameworks rely on massive, pre-trained 3D networks to predict geometry.