  Computer vision technologies markedly enhance the automation capabilities of
robotic-assisted minimally invasive surgery (RAMIS) through advanced tool
tracking, detection, and localization. However, the limited availability of
comprehensive surgical datasets for training represents a significant challenge
in this field. This research introduces a novel method that employs 3D Gaussian
Splatting to generate synthetic surgical datasets. We propose a method for
extracting and combining 3D Gaussian representations of surgical instruments
and background operating environments, transforming and combining them to
generate high-fidelity synthetic surgical scenarios. We developed a data
recording system capable of acquiring images alongside tool and camera poses in
a surgical scene. Using this pose data, we synthetically replicate the scene,
thereby enabling direct comparisons of the synthetic image quality (29.592
PSNR). As a further validation, we compared two YOLOv5 models trained on the
synthetic and real data, respectively, and assessed their performance in an
unseen real-world test dataset. Comparing the performances, we observe an
improvement in neural network performance, with the synthetic-trained model
outperforming the real-world trained model by 12%, testing both on real-world
data.
