Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Learning Independent Object Motion from Unlabelled Stereoscopic Videos (CVPR 2019)
Authors: Zhe Cao, Abhishek Kar, Christian Haene, Jitendra Malik
Affiliations: UC Berkeley, Fyusion Inc, Google
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 \(M=\{M^1,..., M^j\}\)
- For each region of interest RoI, predict a per-object flow map using a RCNN
- Also predict a object mask for each RoI
- Construct a full 3D scene flow map using the per-object flow maps.
Self Supervision and Loss Functions
- View Synthesis
- Geometric consistency: The depth values of the warped image and the reference image should match
- Left Right consistency \(L^{lr}\)
- RoI Loss \(L^{roi}\)
- Full image based loss \(L^{t}\)