  Stereo matching plays a crucial role in 3D perception and scenario
understanding. Despite the proliferation of promising methods, addressing
texture-less and texture-repetitive conditions remains challenging due to the
insufficient availability of rich geometric and semantic information. In this
paper, we propose a lightweight volume refinement scheme to tackle the texture
deterioration in practical outdoor scenarios. Specifically, we introduce a
depth volume supervised by the ground-truth depth map, capturing the relative
hierarchy of image texture. Subsequently, the disparity discrepancy volume
undergoes hierarchical filtering through the incorporation of depth-aware
hierarchy attention and target-aware disparity attention modules. Local fine
structure and context are emphasized to mitigate ambiguity and redundancy
during volume aggregation. Furthermore, we propose a more rigorous evaluation
metric that considers depth-wise relative error, providing comprehensive
evaluations for universal stereo matching and depth estimation models. We
extensively validate the superiority of our proposed methods on public
datasets. Results demonstrate that our model achieves state-of-the-art
performance, particularly excelling in scenarios with texture-less images. The
code is available at https://github.com/ztsrxh/DVANet.
