Visual Odometry with Transformers

Modern monocular visual odometry methods typically combine pre-trained deep learning components with optimization modules, resulting in complex pipelines that rely heavily on camera calibration and hyperparameter tuning, and often struggle in unseen real-world scenarios. Recent large-scale 3D models trained on massive amounts of multi-modal data have partially alleviated these challenges, providing generalizable dense reconstruction and camera pose estimation. Still, they remain limited in handling long videos and providing accurate per-frame estimates, which are required for visual odometry. In this work, we demonstrate that monocular visual odometry can be addressed effectively in an end-to-end manner, thereby eliminating the need for handcrafted components such as bundle adjustment, feature matching, camera calibration, or dense 3D reconstruction. We introduce VoT, short for Visual odometry Transformer, which processes sequences of monocular frames by extracting features and modeling global relationships through temporal and spatial attention. Unlike prior methods, VoT directly predicts camera motion without estimating dense geometry and relies solely on camera poses for supervision. The framework is modular and flexible, allowing seamless integration of various pre-trained encoders as feature extractors. Experimental results demonstrate that VoT scales effectively with larger datasets, benefits substantially from stronger pre-trained backbones, generalizes across diverse camera motions and calibration settings, and outperforms traditional methods while running more than 3 times faster. The code will be released.