Researchers at Stanford University have developed a Web service called Make3D that lets users turn a single two-dimensional image of an outdoor scene into an immersive 3-D model. This gives users the ability to easily create a more realistic visual representation of a photo–one that lets viewers fly around the scene.
To convert the still images into 3-D visualizations, Andrew Ng, an assistant professor of computer science, and Ashutosh Saxena, a doctoral student in computer science, developed a machine-learning algorithm that associates visual cues, such as color, texture, and size, with certain depth values based on what they have learned from studying two-dimensional photos paired with 3-D data. For example, says Ng, grass has a distinctive texture that makes it look very different close up than it does from far away. The algorithm learns that the progressive change in texture gives clues to the distance of a patch of grass.
Larry Davis, a professor and chair of the computer-science department at the University of Maryland, in College Park, says that turning a single image into a 3-D model has been a hard and mathematically complicated problem in computer vision, and that even though Make3D gets things wrong, it often produces remarkable results.
Make3D is not the first site to extract a 3-D model from a single image. Researchers at Carnegie Mellon University (CMU) launched Fotowoosh in May 2007. (See “A New Dimension for Your Photos.”) But Fotowoosh’s algorithm is limited because it labels the orientation of surfaces as either horizontal or vertical, without taking into account such things as mountain slopes, rooftops, or even staircases, says Ng. Based on these restrictive assumptions, Fotowoosh infers the depth of a scene to reconstruct the image.
Derek Hoiem, who built Fotowoosh’s algorithm, admits that unlike Make3D, his work does not give a good estimation of depth. “I can say this point [in an image] is a lot further [away from the foreground] than that point, but I can’t say that this point is five meters away,” he explains. Hoiem developed Fotowoosh with CMU faculty members Alexei Efros and Martial Hebert. (Hoiem is currently a postdoctoral fellow at the Beckman Institute at the University of Illinois at Urbana-Champaign.)
Saxena says that by knowing the depth values of objects in a scene, Make3D creates a higher-quality 3-D model. In a survey performed by the Stanford researchers in 2006, users preferred Make3D’s images twice as often as images created using Fotowoosh.
Hoiem says that he has been trying to extend his work to deal with arbitrary angles, but he has yet to come up with a solution and is “impressed” with the Stanford researchers’ work.
To build Make3D’s algorithm, the Stanford researchers used a laser scanner to estimate the distance of every pixel or point in a two-dimensional image. That 3-D information was coupled with the image and reviewed by the algorithm so that it could learn to correlate visual properties in the image with depth values. For example, it will learn that a large blue pad is probably part of the sky and farther away, says Saxena. There are thousands of such visual properties that humans unconsciously use to determine depth. The Make3D algorithm learns these kinds of rules and processes images accordingly, he says.