Geometry OR Tracker: Universal Geometric Operating Room Tracking

Yihua Shao, Kang Chen, Feng Xue, Siyu Chen, Long Bai +4 more
2/28/2026
cs.CVcs.AI

No confident code implementation found for this paper.

Abstract

In operating rooms (OR), world-scale multi-view 3D tracking supports downstream applications such as surgeon behavior recognition, where physically meaningful quantities such as distances and motion statistics must be measured in meters. However, real clinical deployments rarely satisfy the geometric prerequisites for stable multi-view fusion and tracking: camera calibration and RGB-D registration are always unreliable, leading to cross-view geometric inconsistency that produces "ghosting" during fusion and degrades 3D trajectories in a shared OR coordinate frame. To address this, we introduce Geometry OR Tracker, a two-stage pipeline that first rectifies imprecise calibration into a scaleconsistent and geometrically consistent camera setup with a single global scale via a Multi-view Metric Geometry Rectification module, and then performs Occlusion-Robust 3D Point Tracking directly in the unified OR world frame. On the MM-OR benchmark, improved geometric consistency translates into tracking gains: our rectification front-end reduces cross-view depth disagreement by more than 30$\times$ compared to raw calibration. Ablation studies further demonstrate the relationship between calibration quality and tracking accuracy, showing that improved geometric consistency yields stronger world-frame tracking.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{shao2026geometry,
  title  = {Geometry OR Tracker: Universal Geometric Operating Room Tracking},
  author = {Yihua Shao and Kang Chen and Feng Xue and Siyu Chen and Long Bai and Hongyuan Yu and Hao Tang and Jinlin Wu and Nassir Navab},
  year   = {2026},
  eprint = {2603.00560},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.00560v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2603.00560.svg)](https://codeofpaper.com/paper/2603.00560)

Discussion