  Stereo videos for the dynamic scenes often show unpleasant blurred effects
due to the camera motion and the multiple moving objects with large depth
variations. Given consecutive blurred stereo video frames, we aim to recover
the latent clean images, estimate the 3D scene flow and segment the multiple
moving objects. These three tasks have been previously addressed separately,
which fail to exploit the internal connections among these tasks and cannot
achieve optimality. In this paper, we propose to jointly solve these three
tasks in a unified framework by exploiting their intrinsic connections. To this
end, we represent the dynamic scenes with the piece-wise planar model, which
exploits the local structure of the scene and expresses various dynamic scenes.
Under our model, these three tasks are naturally connected and expressed as the
parameter estimation of 3D scene structure and camera motion (structure and
motion for the dynamic scenes). By exploiting the blur model constraint, the
moving objects and the 3D scene structure, we reach an energy minimization
formulation for joint deblurring, scene flow and segmentation. We evaluate our
approach extensively on both synthetic datasets and publicly available real
datasets with fast-moving objects, camera motion, uncontrolled lighting
conditions and shadows. Experimental results demonstrate that our method can
achieve significant improvement in stereo video deblurring, scene flow
estimation and moving object segmentation, over state-of-the-art methods.
