Structure
An anchor graph derived from preoperative CT provides dense anatomical priors for coarse-to-fine localization.
ACM Multimedia 2026 Rio de Janeiro
Geometry-Aware Camera Localization for Bronchoscopy
Fusing preoperative airway geometry with intraoperative video for accurate, stable, and real-time 6-DoF bronchoscope localization.
Chinese Academy of Sciences · Sun Yat-sen University
Why geometry matters
One framework, three complementary scales.
Camera localization in bronchoscopy is challenged by repetitive anatomy, limited texture, and strict latency requirements. GABL injects explicit geometric supervision at the structural, temporal, and appearance levels, combining preoperative CT priors with intraoperative observations in a unified localization framework.
Motion
A causal Transformer models temporal dynamics to stabilize predictions and reduce trajectory drift.
Appearance
RGB-depth matching aligns intraoperative observations with rendered geometry in a shared representation space.
Framework
Geometry-aware supervision from CT to trajectory.
Results
Accurate enough to navigate. Fast enough to guide.
7.01mm
Translation ATE
8.37% lower
29.56deg
Rotation ATE
31.76% lower
61.04%
SR-5
Best reported
33.6FPS
Inference speed
Real-time
Qualitative localization
Predicted trajectories closely follow the airway geometry.
Citation
Build on GABL.
@inproceedings{chen2026gabl,
title = {Geometry-Aware Camera Localization for Bronchoscopy},
author = {Chen, Lumin and Tian, Qingyao and Li, Jinpeng and
Jiang, Haoyu and Liao, Huai and Huang, Xinyan and
Liu, Hongbin and Yi, Dong},
booktitle = {Proceedings of the 34th ACM International Conference
on Multimedia},
year = {2026},
doi = {10.1145/3767308.3835259}
}