Barn owls move their heads in very particular motions, compensating for the quasi-immovability of their eyes. These efficient predators often perform peering side-to-side head motions when scanning their surroundings and seeking prey. In this work, we use the head movements of barn owls as a model to bridge between biological active vision and machine vision. The biomotions are measured and used to actuate a specially built robot equipped with a depth camera for scanning. We hypothesize that the biomotions improve scan accuracy of static objects. Our experiments show that barn owl biomotion-based trajectories consistently improve scan accuracy when compared to intuitive scanning motions. This constitutes proof-of-concept evidence that the vision of robotic systems can be enhanced by bio-inspired viewpoint manipulation. Such biomimetic scanning systems can have many applications, e.g. manufacturing inspection or in autonomous robots.