Researchers have developed a new technique, called MonoCon, that improves the ability of artificial intelligence (AI) programs to identify three-dimensional (3D) objects, and how those objects relate to each other in space, using two-dimensional (2D) images. For example, the work would help the AI used in autonomous vehicles navigate in relation to other vehicles using the 2D images it receives from an onboard camera.
From an article by North Carolina State University.
“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.
For the complete article on MonoCon CLICK HERE.
Note – If you liked this post click here to stay informed of all of the 3D laser scanning, geomatics, UAS, autonomous vehicle, Lidar News and more. If you have an informative 3D video that you would like us to promote, please forward to editor@lidarnews.com and if you would like to join the Younger Geospatial Professional movement click here.
