High-Quality Virtual Single-Viewpoint Surgical Video: Geometric Autocalibration of Multiple Cameras in Surgical Lights
This addresses occlusion issues in surgical video for medical professionals, but it is incremental as it automates an existing multi-camera setup.
The paper tackles the problem of occlusion in surgical video by automating the alignment of multiple cameras on a surgical light, resulting in a stabilized video with less occlusion that outperforms conventional approaches in quantitative results and a user study with medical doctors.
Occlusion-free video generation is challenging due to surgeons' obstructions in the camera field of view. Prior work has addressed this issue by installing multiple cameras on a surgical light, hoping some cameras will observe the surgical field with less occlusion. However, this special camera setup poses a new imaging challenge since camera configurations can change every time surgeons move the light, and manual image alignment is required. This paper proposes an algorithm to automate this alignment task. The proposed method detects frames where the lighting system moves, realigns them, and selects the camera with the least occlusion. This algorithm results in a stabilized video with less occlusion. Quantitative results show that our method outperforms conventional approaches. A user study involving medical doctors also confirmed the superiority of our method.