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SfM-Net: Learning of Structure and Motion from Video
[article]
2017
arXiv
pre-print
We propose SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates. The model can be trained with
arXiv:1704.07804v1
fatcat:e3qxconotfgajazkuxjnz5yyp4