Current stereo matching methods benefit a lot from the precise stereo estimation with Convolutional Neural Networks (CNNs). Nevertheless, patch-based siamese networks rely on the implicit assumption of constant depth within a window, which does not hold for slanted surfaces. Existing methods for handling slanted patches focus on post-processing. In contrast, we propose a novel module for matching cost networks to overcome this bias. Slanted objects appear horizontally stretched between stereo pairs, suggesting that the feature extraction in the horizontal direction should be different from that in the vertical direction. To tackle this distortion, we utilize asymmetric convolutions in our proposed module. Experimental results show that the proposed module in matching cost networks can achieve higher accuracy with fewer parameters compared to conventional methods.

We propose a computing method for linear convolution and linear correlation between sequences using discrete cosine transform (DCT). Zero-padding is considered as well as linear convolution using discrete Fourier transform (DFT). Analyzing the circular convolution between symmetrically extended sequences, we derive the condition for zero-padding before and after the sequences. The proposed method can calculate linear convolution for any filter and also calculate linear correlation without reversing one of the input sequences. The computational complexity of the proposed method is lower than that of linear convolution using DFT.