Learning Independent Object Motion from Unlabelled Stereoscopic Videos: Difference between revisions

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==Method==
==Method==
;Key Contributions
* Learning with limited supervision
* Factoring the scene into independent moving objects (main idea of the paper)
* Designing a network architecture using place sweep volumes
;Inputs:
* Image pairs \(\{(I_1^l, I_1^r),..., (I_n^l, I_n^r)\}\) from unlabelled stereo videos
* Object bounding boxes \(B = \{B^1,..., B^j\}\) on the left image \(I_t^l\) from off-the-shelf object detectors
;Goal/Outputs:
* Dense depth map \(D\)
* 3D flow fields \(F = \{F^1,..., F^j\}\)
* Instance masks \(M=\{M^1,..., M^j\}\)


==Architecture==
==Architecture==