# High-Quality Virtual Single-Viewpoint Surgical Video: Geometric Autocalibration of Multiple Cameras in Surgical Lights

arXiv: https://arxiv.org/abs/2503.03558 · HTML: https://mlsanity.com/paper/2503.03558/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings](https://mlsanity.com/paper/2303.15947/) (2023)

## Built on by (most cited first, 2 of 2)

- [Disturbance-Free Surgical Video Generation from Multi-Camera Shadowless Lamps for Open Surgery](https://mlsanity.com/paper/2512.08577/) (2025)
- [TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis](https://mlsanity.com/paper/2504.06527/) (2025)

## Abstract (arXiv)

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.