Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction
Not on the map. Why?
Abstract (arXiv)
The purpose of this study is to develop an automated and accurate external camera calibration method for multi-camera systems used in 3D surgical scene reconstruction (3D-SSR), eliminating the need for operator intervention or specialized expertise. The method specifically addresses the problem of limited overlapping fields of view caused by significant variations in optical zoom levels and camera locations. We contribute a novel, fast, and fully automatic calibration method based on the projection of multi-scale markers (MSMs) using a ceiling-mounted projector. MSMs consist of 2D patterns projected at varying scales, ensuring accurate extraction of well distributed point correspondences across significantly different viewpoints and zoom levels. Validation is performed using both synthetic and real data captured in a mock-up OR, with comparisons to traditional manual marker-based methods as well as markerless calibration methods. The method achieves accuracy comparable to manual, operator-dependent calibration methods while exhibiting higher robustness under conditions of significant differences in zoom levels. Additionally, we show that state-of-the-art Structure-from-Motion (SfM) pipelines are ineffective in 3D-SSR settings, even when additional texture is projected onto the OR floor. The use of a ceiling-mounted entry-level projector proves to be an effective alternative to operator-dependent, traditional marker-based methods, paving the way for fully automated 3D-SSR.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
5 of 28 references.
References · 5
- 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
- 2024Global Structure-from-Motion Revisitedcited by 215
- 2021Robotic Surgery With Lean Reinforcement Learningcited by 11
- 2024Creating a Digital Twin of Spinal Surgery: A Proof of Conceptcited by 9
- 2023ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Roomcited by 6
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Roboticscited by 1
- 2025RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systemscited by 1
- 2026A Multi-View Pipeline and Benchmark Dataset for 3D Hand Pose Estimation in Surgerycited by 0
- 2026Geometry OR Tracker: Universal Geometric Operating Room Trackingcited by 0
- 2026Geometry OR Tracker: Universal Geometric Operating Room Tracking28 Feb 2026
- 2026A Multi-View Pipeline and Benchmark Dataset for 3D Hand Pose Estimation in Surgery22 Jan 2026
- 2025RocSync: Millisecond-Accurate Temporal Synchronization for Heterogeneous Camera Systems18 Nov 2025
- 2025AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics15 May 2025