  Visual odometry plays a crucial role in endoscopic imaging, yet the scarcity
of realistic images with ground truth poses poses a significant challenge.
Therefore, domain adaptation offers a promising approach to bridge the
pre-operative planning domain with the intra-operative real domain for learning
odometry information. However, existing methodologies suffer from
inefficiencies in the training time. In this work, an efficient neural style
transfer framework for endoscopic visual odometry is proposed, which compresses
the time from pre-operative planning to testing phase to less than five
minutes. For efficient traing, this work focuses on training modules with only
a limited number of real images and we exploit pre-operative prior information
to dramatically reduce training duration. Moreover, during the testing phase,
we propose a novel Test Time Adaptation (TTA) method to mitigate the gap in
lighting conditions between training and testing datasets. Experimental
evaluations conducted on two public endoscope datasets showcase that our method
achieves state-of-the-art accuracy in visual odometry tasks while boasting the
fastest training speeds. These results demonstrate significant promise for
intra-operative surgery applications.
