Variance-aware spatial decoupling attention for stereo matching

Abstract

The stereo matching algorithms which are based on feature matching build cost volume to regression disparity. Such cost volume shows a regular data distribution along the disparity dimension. The pixels with proper matching should have a single-peak distribution, while the pixels in the ill-conditioned regions have a multi-peak distribution. Using the variance of disparity dimension data, ill-conditioned regions can be identified. In this paper, an attention mechanism based on variance features is proposed, which promotes the neural network to deal with complicated regions. By decoupling the space of the image, a linear complexity attention mechanism is realized. It can be embedded into the algorithm based on feature matching with a slight increase in inference time, and a suitable trade-off is achieved in precision and speed. By embedding the module into BGNet, the accuracy of the designed model has been improved by 10 %, meanwhile, competitive performance has been achieved.