Take a moment and have a deep breath before you get excited about full 3d reconstruction with a single image. That isn't what this is. Watch the video in the

Take a moment and have a deep breath before you get excited about full 3d reconstruction with a single image. That isn't what this is. Watch the video in the bottom right corner, entitled "Comparison with State of the art". Now go and rewatch the examples and actually look at the edges of the objects as they move the camera 'a bit'. You'll see a tonne of artifacting. Less, clearly, than the existing state of the art, so I tip my hat to the efforts here. ...but all this is doing is generating an 'empty gap' generated by perspective and then basically using the equivalent of photoshop's content aware fill to fill that gap with plausible pixels. Since humans don't really pay much attention to edge details, it's quite plausible. To quote the paper: > In this work we present a new learning-based method that generates a 3D photo from an RGB-D input. Ie. This work is taking a depth image as the input and working on that to generate a 3d photo, rebuilding the full content of each 2d layer in the image. Ie. The output is not a 3d model, it is a series of 2D images at depth intervals, where the occluded content in each layer is in-painted (ie. generated artifically). (NB. The 'from a single source image' work used here is not novel; they're just using existing approaches to estimate a depth image)