"We live in a 3D world, but when you take a picture, it records that world in a 2D image," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at North Carolina State University.

"AI programs receive visual input from cameras. So if we want AI to interact with the world, we need to ensure that it is able to interpret what 2D images can tell it about 3D space. In this research, we are focused on one part of that challenge: how we can get AI to accurately recognize 3D objects -- such as people or cars -- in 2D images, and place those objects in space."

While the work may be important for autonomous vehicles, it also has applications for manufacturing and robotics.

In the context of autonomous vehicles, most existing systems rely on lidar -- which uses lasers to measure distance -- to navigate 3D space. However, lidar technology is expensive. And because lidar is expensive, autonomous systems don't include much redundancy. For example, it would be too expensive to put dozens of lidar sensors on a mass-produced driverless car.

"But if an autonomous vehicle could use visual inputs to navigate through space, you could build in redundancy," Wu says. "Because cameras are significantly less expensive than lidar, it would be economically feasible to include additional cameras -- building redundancy into the system and making it both safer and more robust.

"That's one practical application. However, we're also excited about the fundamental advance of this work: that it is possible to get 3D data from 2D objects."

Specifically, MonoCon is capable of identifying 3D objects in 2D images and placing them in a "bounding box," which effectively tells the AI the outermost edges of the relevant object.

MonoCon builds on a substantial amount of existing work aimed at helping AI programs extract 3D data from 2D images. Many of these efforts train the AI by "showing" it 2D images and placing 3D bounding boxes around objects in the image. These boxes are cuboids, which have eight points -- think of the corners on a shoebox. During training, the AI is given 3D coordinates for each of the box's eight corners, so that the AI "understands" the height, width and length of the "bounding box," as well as the distance between each of those corners and the camera. The training technique uses this to teach the AI how to estimate the dimensions of each bounding box and instructs the AI to predict the distance between the camera and the car. After each prediction, the trainers "correct" the AI, giving it the correct answers. Over time, this allows the AI to get better and better at identifying objects, placing them in a bounding box, and estimating the dimensions of the objects.