  Being a crucial task of autonomous driving, Stereo matching has made great
progress in recent years. Existing stereo matching methods estimate disparity
instead of depth. They treat the disparity errors as the evaluation metric of
the depth estimation errors, since the depth can be calculated from the
disparity according to the triangulation principle. However, we find that the
error of the depth depends not only on the error of the disparity but also on
the depth range of the points. Therefore, even if the disparity error is low,
the depth error is still large, especially for the distant points. In this
paper, a novel Direct Depth Learning Network (DDL-Net) is designed for stereo
matching. DDL-Net consists of two stages: the Coarse Depth Estimation stage and
the Adaptive-Grained Depth Refinement stage, which are all supervised by depth
instead of disparity. Specifically, Coarse Depth Estimation stage uniformly
samples the matching candidates according to depth range to construct cost
volume and output coarse depth. Adaptive-Grained Depth Refinement stage
performs further matching near the coarse depth to correct the imprecise
matching and wrong matching. To make the Adaptive-Grained Depth Refinement
stage robust to the coarse depth and adaptive to the depth range of the points,
the Granularity Uncertainty is introduced to Adaptive-Grained Depth Refinement
stage. Granularity Uncertainty adjusts the matching range and selects the
candidates' features according to coarse prediction confidence and depth range.
We verify the performance of DDL-Net on SceneFlow dataset and DrivingStereo
dataset by different depth metrics. Results show that DDL-Net achieves an
average improvement of 25% on the SceneFlow dataset and $12\%$ on the
DrivingStereo dataset comparing the classical methods. More importantly, we
achieve state-of-the-art accuracy at a large distance.
