UDepth can estimate parallax computationally for each pixel in the image where real physical distances can be reconstructed. Stereo camera systems reconstruct depth in this way, using parallax, the apparently different positioning of objects when seen from different viewpoints. Computer vision systems meanwhile can reconstruct three-dimensional depth information from two distinct image views by correlating their pixels. Human brains perceive depth using offset image signals from the left and right eye. Depth sensing is a valuable tool for 3D photos generation since it can determine the 3D information about a scene from 2D images. The real-time infrared (IR) active stereo depth sensor leverages machine learning techniques and supports various features for the generation of 3D photos. Google recently released a demo app that visualizes a real-time point cloud from uDepth, the stereo depth sensor built into the Pixel 4 and Pixel 4 XL Google smartphones. Synced has identified a few significant technical advancements in the 3D photo field that we believe may be of interest to our readers. The FaceID process for example employs an array of flood illuminators, sensors and an infrared camera to generate the 3D facial depth map it uses for verification and unlock. Today’s smartphone authentication systems also rely on 3D image capability.

Computer vision (CV) has achieved remarkable progress across various subfields and real-world applications, but few bring the everyday user as visually appreciable an effect as the trending medium of 3D photo generation’s ability to deepen the immersive experience of a captured moment.Īnother common use of 3D images is in AR and VR applications, where they can provide viewers a unique lifelike experience. Facebook isn’t the only tech giant using AI to generate 3D photos - in recent months, Google has introduced its own AI techniques for generating 3D photos from 2D images.

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