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A fun experiment would be to try to build something that predicts complete depth maps from single images alone. (ie: train on images with stereo depth maps, tr
by a-dub 8y ago
A fun experiment would be to try to build something that predicts complete depth maps from single images alone. (ie: train on images with stereo depth maps, try to predict them from just one side)
- sorenjan 8y agoCalled depth estimation from single image. There's ongoing research, here's one example: [0] Using only the segmented 2D image is what the selfie cam does by the way, at least on Pixel 2 [1]. [0] https://papers.nips.cc/paper/5539-depth-map-prediction-from-a-single-image-using-a-multi-scale-deep-network.pdf https://papers.nips.cc/paper/5539-depth-map-prediction-from-... [1] https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-a...
- a-dub 8y agoCool!
- state_less 8y agoIt would be fun to estimate depth maps from single images. And for the next act, estimate scene graphs from a single image where the understanding of what is being looked at and the position is estimated. For example, a fingernail connected to a jointed finger, connected to a hand, to an arm, to a body and so on. With dimensions, locations and rotations relative to each other. One could render a humanoid model at 120 frames (or whatever the gpu would allow) a second using a set of estimated scene graphs trying to minimize error between the image and the rendering. Add more models (tvs, cars, signs, letters, etc...) over time if the technique shows promise.