MonoXiver helps AI turn 2D into 3D imagery for autonomous vehicle cameras

According to Tianfu Wu, co-author of a paper on the work, existing techniques for extracting 3D information from 2D images are good, but not good enough.

“Our new method…can be used in conjunction with existing techniques and makes them significantly more accurate,” said Wu, an associate professor of electrical and computer engineering at NC State.

Cameras are less expensive than other tools used to navigate 3D spaces, such as LIDAR that relies on lasers to measure distance. Designers of autonomous vehicles can install multiple cameras to build redundancy into the system, but that is only useful if the AI in the autonomous vehicle can extract 3D navigational information from the 2D images taken by a camera.

Existing techniques that extract 3D data from 2D images – such as the MonoCon technique developed by Wu and his collaborators – make use of so-called ‘bounding boxes’. These techniques train AI to scan a 2D image and place 3D bounding boxes around objects in the 2D image, such as each car on a street. These boxes are cuboids with eight points. The bounding boxes help the AI estimate the dimensions of the objects in an image, and where each object is in relation to other objects.