Competitive Collaboration_ Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation.pdf
Abstract
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our
key insight is that these four fundamental vision problems are
coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the
solutions can reinforce each other. We go beyond previous
work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end,
we introduce Competitive Collaboration, a framework that
facilitates the coordinated training of multiple specialized
neural networks to solve complex problems. Competitive
Collaboration works much like expectation-maximization,
but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and
as collaborators through a moderator that assigns pixels to
be either static or independently moving. Our novel method
integrates all these problems in a common framework and
simultaneously reasons about the segmentation of the scene
into moving objects and the static background, the camera
motion, depth of the static scene structure, and the optical
flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among
joint unsupervised methods on all sub-problems. .