ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
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
68b7f86c295f15ff6091153d
Ganlin Zhang
claimed_verified
2025-09-03T08:25:46.109Z
6501764b35ec971762b57317
/avatars/47d4fa98df0df3d02428b4c3347e239c.svg
Ganlin Zhang
user
zhangganlin
68b7f86c295f15ff6091153e
Shenhan Qian
68b7f86c295f15ff6091153f
Xi Wang
68b7f86c295f15ff60911540
Daniel Cremers
ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
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
ViSTA-SLAM is a real-time monocular SLAM system that uses a lightweight STA model for pose estimation and pointmap regression, and a Sim(3) pose graph for drift correction, achieving superior tracking and reconstruction.
monocular visual SLAM symmetric two-view association STA model relative camera poses local pointmaps Sim(3) pose graph loop closures camera tracking dense 3D reconstruction