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It has very much been fitted. The supplementary material describes the training set as 5 pictures each of 2M different scenes of the type you can see in the pap
by yagyu 8y ago
It has very much been fitted. The supplementary material describes the training set as 5 pictures each of 2M different scenes of the type you can see in the paper (square room with random objects).
So to extrapolate wildly, it seems reasonable that getting similar results for, say, real-world bedrooms, you'd need to take around 5 pics each of 2M bedrooms, and record the location and angle of the camera for each picture.
Edit: and I didn't mean to sound negative, using artificially generated rooms to develop the method is a great idea, and the next step will be narrow domain specific applications (they mention eg robotic arms) where it's feasible to automatically collect enough data for a task, and somewhere in the future we may have the data and compute to sample the distribution of real world environments in decent resolution..
- chrisfosterelli 8y agoI'd say much more than that, since real-world bedrooms likely have a much more complicated representation than more simple generated rooms.
- yagyu 8y agoI agree with you that the estimate is conservative, and would depend strongly on image resolution and how broad your distribution of bedrooms is - only modern US style, or also 40 year old Japanese houses?
- visarga 8y agoThis could be achieved with a drone camera and positioning sensors.
- maffydub 8y agoFYI, for artificially generated rooms, there is already the SceneNet RGB-D dataset - "5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth" https://robotvault.bitbucket.io/scenenet-rgbd.html https://robotvault.bitbucket.io/scenenet-rgbd.html I think there are only 15k different rooms, rather than 2M you suggested, though!