ViSTA-SLAM: Visual SLAM With Symmetric Two-View Association
MCML Authors
Xi Wang
Dr.
JRG Leader Egocentric Vision
Daniel Cremers
Prof. Dr.
Director
Abstract
Xi Wang
Dr.
JRG Leader Egocentric Vision
Daniel Cremers
Prof. Dr.
Director
Abstract
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.
inproceedings ZQP+26
3DV 2026
13th International Conference on 3D Vision. Vancouver, Canada, Mar 20-23, 2026. To be published. Preprint available.Authors
G. Zhang • S. Qian • X. Wang • D. CremersLinks
URL GitHubResearch Area
BibTeXKey: ZQP+26