SLAM-Former: Putting SLAM into One Transformer

We present SLAM-Former, a neural approach that integratesfull SLAM capabilities into a single transformer. Similar to traditionalSLAM systems, SLAM-Former comprises both a frontend and a back-end that operate in tandem. The frontend processes sequential monoc-ular images in real-time for incremental mapping and tracking, whilethe backend performs global refinement to ensure a geometrically con-sistent result. This alternating execution allows the frontend and back-end to mutually promote one another, enhancing overall system perfor-mance. Comprehensive experimental results demonstrate that SLAM-Former achieves superior or highly competitive performance comparedto state-of-the-art dense SLAM methods.