  Cost aggregation is a key component of stereo matching for high-quality depth
estimation. Most methods use multi-scale processing to downsample cost volume
for proper context information, but will cause loss of details when upsampling.
In this paper, we present a content-aware inter-scale cost aggregation method
that adaptively aggregates and upsamples the cost volume from coarse-scale to
fine-scale by learning dynamic filter weights according to the content of the
left and right views on the two scales. Our method achieves reliable detail
recovery when upsampling through the aggregation of information across
different scales. Furthermore, a novel decomposition strategy is proposed to
efficiently construct the 3D filter weights and aggregate the 3D cost volume,
which greatly reduces the computation cost. We first learn the 2D similarities
via the feature maps on the two scales, and then build the 3D filter weights
based on the 2D similarities from the left and right views. After that, we
split the aggregation in a full 3D spatial-disparity space into the aggregation
in 1D disparity space and 2D spatial space. Experiment results on Scene Flow
dataset, KITTI2015 and Middlebury demonstrate the effectiveness of our method.
