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more like automatic blurry copy of original image :( All the examples are single frames from movies, so why bother when you can extract perfect 3d using multitu
by rasz_pl 11y ago
more like automatic blurry copy of original image :(
All the examples are single frames from movies, so why bother when you can extract perfect 3d using multitude of SFM methods (PTAM/SVO/etc) using whole scenes?
- deleted 11y ago[deleted]
- ericjang 11y agoI think the authors chose the wrong narrative to discuss in their motivation for the paper. Anyway, it's easier to just shoot a movie / VR content with multiple cameras than go through frame-by-frame to make sure the AI isn't introducing blurry regions etc. However, being able to extract 3D scene features from a still 2D image is a super useful feature to have. Perhaps in the near future, inference models for image-based tasks (i.e. ImageNet, robotics) will make use of 3D features learned during end-to-end training. The disparity predictions generated here remind me of spatial transformer layers http://arxiv.org/abs/1506.02025 http://arxiv.org/abs/1506.02025 which Deepmind is using pretty heavily in their sequential attention models. I wonder if they would offer an improvement over the deconv layers.
- billconan 11y agoI think this method managed to tell the foreground from the background. the background is shifted more than the foreground.