ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
Authors: name: Ganlin Zhang, name: Shenhan Qian, name: Xi Wang, name: Daniel Cremers

We present ViSTA-SLAM as a real-time monocular visual SLAM system that
operates without requiring camera intrinsics, making it broadly applicable
across diverse camera setups. At its core, the system employs a lightweight
symmetric two-view association (STA) model as the frontend, which
simultaneously estimates relative camera poses and regresses local pointmaps
from only two RGB images. This design reduces model complexity significantly,
the size of our frontend is only 35\% that of comparable state-of-the-art
methods, while enhancing the quality of two-view constraints used in the
pipeline. In the backend, we construct a specially designed Sim(3) pose graph
that incorporates loop closures to address accumulated drift. Extensive
experiments demonstrate that our approach achieves superior performance in both
camera tracking and dense 3D reconstruction quality compared to current
methods. Github repository: https://github.com/zhangganlin/vista-slam