  Are we ready to segment consumer stereo videos? The amount of this data type
is rapidly increasing and encompasses rich information of appearance, motion
and depth cues. However, the segmentation of such data is still largely
unexplored. First, we propose therefore a new benchmark: videos, annotations
and metrics to measure progress on this emerging challenge. Second, we evaluate
several state of the art segmentation methods and propose a novel ensemble
method based on recent spectral theory. This combines existing image and video
segmentation techniques in an efficient scheme. Finally, we propose and
integrate into this model a novel regressor, learnt to optimize the stereo
segmentation performance directly via a differentiable proxy. The regressor
makes our segmentation ensemble adaptive to each stereo video and outperforms
the segmentations of the ensemble as well as a most recent RGB-D segmentation
technique.
