  A depth map can be represented by a set of learned bases and can be
efficiently solved in a closed form solution. However, one issue with this
method is that it may create artifacts when colour boundaries are inconsistent
with depth boundaries. In fact, this is very common in a natural image. To
address this issue, we enforce a more strict model in depth recovery: a
piece-wise planar model. More specifically, we represent the desired depth map
as a collection of 3D planar and the reconstruction problem is formulated as
the optimization of planar parameters. Such a problem can be formulated as a
continuous CRF optimization problem and can be solved through particle based
method (MP-PBP) \cite{Yamaguchi14}. Extensive experimental evaluations on the
KITTI visual odometry dataset show that our proposed methods own high
resistance to false object boundaries and can generate useful and visually
pleasant 3D point clouds.
