  Convolutional neural networks(CNN) have been shown to perform better than the
conventional stereo algorithms for stereo estimation. Numerous efforts focus on
the pixel-wise matching cost computation, which is the important building block
for many start-of-the-art algorithms. However, those architectures are limited
to small and single scale receptive fields and use traditional methods for cost
aggregation or even ignore cost aggregation. Differently we take them both into
consideration. Firstly, we propose a new multi-scale matching cost computation
sub-network, in which two different sizes of receptive fields are implemented
parallelly. In this way, the network can make the best use of both variants and
balance the trade-off between the increase of receptive field and the loss of
detail. Furthermore, we show that our multi-dimension aggregation sub-network
which containing 2D convolution and 3D convolution operations can provide rich
context and semantic information for estimating an accurate initial disparity.
Finally, experiments on challenging stereo benchmark KITTI demonstrate that the
proposed method can achieve competitive results even without any additional
post-processing.
