用天花板投影标定多摄像头,自动实现高精度手术场景三维重建。
Automatic Calibration of a Multi-Camera System with Limited Overlapping Fields of View for 3D Surgical Scene Reconstruction
- 用可变尺度的2D标记图案投影,跨不同视角和焦距精准提取匹配点。
- 在真实模拟手术室中验证,精度媲美人工标定,且抗焦距差异能力更强。
- 适合需全自动三维手术重建的医疗系统,无需专家干预。
本研究旨在开发一种用于3D手术场景重建(3D-SSR)的多摄像头系统自动化外部标定方法,消除对操作员介入或专业技能的需求。该方法针对因光学变焦和相机位置差异导致的视场重叠有限问题。提出一种基于天花板安装投影仪的新型、快速且完全自动化的标定方法,利用多尺度标记(MSMs)——即在不同尺度下投影的2D图案——确保在显著不同的视角和变焦水平下,仍能准确提取分布均匀的点对应关系。通过合成数据和在模拟手术室中采集的真实数据进行验证,与传统手动标记法及无标记标定方法对比。结果表明,该方法精度与依赖人工的标定相当,但在变焦差异显著时展现出更高鲁棒性。此外,我们发现即使在手术室地面额外投射纹理,当前最先进的结构光恢复(SfM)流程在3D-SSR场景中依然无效。使用天花板安装的入门级投影仪,成为替代人工依赖的传统标记法的有效方案,推动了全自动化3D-SSR的发展。
原文摘要 · Abstract (English)
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.
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