“It is ultra-hot, there is a huge buzz,” says Ken Goldberg, a roboticist at the University of California, Berkeley, who is using the technology to improve the ability of AI-enhanced robots to grasp unfamiliar shapes. Goldberg says the technology has “hundreds of applications,” in fields ranging from entertainment to architecture.Ken Goldberg, roboticist, University of California, Berkeleyto capture and generate 3D imagery from a few 2D snapshots, a technique dubbed “neural rendering.
That algorithm exploits the way light travels through the air and performs computations that calculate the density and color of points in 3D space. This makes it possible to convert 2D images into a photorealistic 3D representation that can be viewed from any possible point. Its core is the same sort of neural network as the 2012 image-recognition algorithm, which analyzes the pixels in a 2D image. The new algorithms convert 2D pixels into the 3D equivalent, known as voxels.
“I’ve been doing computer vision for 20 years, but when I saw this video, I was like ‘Wow, this is just incredible,’” saysFor anyone working on computer graphics, Dellaert explains, the approach is a breakthrough. Creating a detailed, realistic 3D scene normally requires hours of painstaking manual work. The new method makes it possible to generate these scenes from ordinary photographs in minutes. It also provides a new way to create and manipulate synthetic scenes.
“I’ve been doing computer vision for 20 years, but when I saw this video, I was like ‘Wow, this is just incredible.’”Dellaert says the speed and variety of ideas that have emerged since then have been breathtaking. Others have used the idea to create moving selfies (or “
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