Purpose: Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow recognition show potential in increasing the quality of patient care, the quantity of video data has surpassed the scale at which images can be manually anonymized. Existing automated 2D anonymization methods under-perform in Operating Rooms (OR), due to occlusions and obstructions. We propose to anonymize multi-view OR recordings using 3D data from multiple camera streams. Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene. We then detect each individual's face in 3D by regressing a parametric human mesh model onto detected 3D human keypoints and aligning the face mesh with the fused 3D point cloud. The mesh model is rendered into every acquired camera view, replacing each individual's face. Results: Our method shows promise in locating faces at a higher rate than existing approaches. DisguisOR produces geometrically consistent anonymizations for each camera view, enabling more realistic anonymization that is less detrimental to downstream tasks. Conclusion: Frequent obstructions and crowding in operating rooms leaves significant room for improvement for off-the-shelf anonymization methods. DisguisOR addresses privacy on a scene level and has the potential to facilitate further research in SDS.

我们提出使用多视角三维数据来匿名化多角度的手术室录像，借助 RGB 和深度图像，将多个摄像头的图像融合成场景的三维点云表示，利用参数化人体网格模型对检测到的三维人体关键点和融合的三维点云进行回归以定位面部，并在每个摄像头视角中渲染出人体网格模型，替换每个个体的面部。我们的方法在定位面部方面比现有方法表现更好，DisguisOR通过对每个摄像头视角进行几何一致性的匿名化，实现了更真实、对下游任务的影响更小的匿名化，该方法旨在解决手术室中的障碍和拥挤对现有匿名化方法的不足，并为Surgical Data Science领域的后续研究提供潜在的便利。

DisguisOR: 术室综合人脸匿名化