The KITTI Vision Benchmark Suite

Stereo image pairs encode 3D scene information
through the correspondences between the left and
right images. Traditional CNN-based methods
typically rely on cost volume techniques to
capture stereo correspondences over large
disparities but suffer from large parameter sizes
and slow computation speeds. Although existing
research has optimized the modeling of stereo
correspondences, the overall network architecture
still needs improvement. To address these
challenges, inspired by the significant
achievements of Kolmogorov-Arnold Networks (KANs)
in terms of accuracy and interpretability, we
propose a new unsupervised stereo matching
network, WT-KAN. This network reshapes the
traditional U-Net architecture by introducing KAN
layers, combining nonlinear learnable activation
functions, wavelet transforms, and axial attention
mechanisms. It enhances the network's ability to
capture details and shapes and establishes long-
range dependencies. WT-KAN can more efficiently
learn complex stereo c

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