3D Convolution Channel Compression for Stereo Matching

In recent years, many deep models for stereo matching employ 3D convolutions for cost aggregation to achieve better performance. However, this approach requires substantial computational and memory resources, limiting the deployment of the model on edge devices. In this paper, we analyze the challenges of channel compression in stereo matching models and adopt a straightforward and efficient compression method. Our method focuses on the channel compression of the cost aggregation module, enabling the model to achieve acceleration on existing hardware, reducing significantly computational cost. We set a hyperparameter \(\kappa \) that decides the compression rate, and compress the channels of each layer in cost aggregation module according to it. This method ensures the consistency for irregular skip connections. We extensively test our method on GwcNet, PSMNet and CFNet on multiple datasets, achieving promising results. We believe that the proposed compression method allows stereo matching models to better balance computational cost and accuracy during various degrees of channel compression, making them more suitable for deployment on edge devices.