  Reconstruction based on the stereo camera has received considerable attention
recently, but two particular challenges still remain. The first concerns the
need to aggregate similar pixels in an effective approach, and the second is to
maintain as much of the available information as possible while ensuring
sufficient accuracy. To overcome these issues, we propose a new 3D
representation method, namely, planecell, that extracts planarity from the
depth-assisted image segmentation and then projects these depth planes into the
3D world. An energy function formulated from Conditional Random Field that
generalizes the planar relationships is maximized to merge coplanar segments.
We evaluate our method with a variety of reconstruction baselines on both KITTI
and Middlebury datasets, and the results indicate the superiorities compared to
other 3D space representation methods in accuracy, memory requirements and
further applications.
