Stereo Matching Network Based on Asymmetric Spatial Pyramid Pooling
摘要
Convolutional Neural Network(CNN) is often used in image processing algorithms because of its excellent representation capabilities,but the process is time-consuming and often results in information loss.To address the problem,this paper proposes a CNN structure based on Asymmetric Spatial Pyramid Pooling(ASPP) model.An ASPP method is designed to be integrated with the stereo matching network to obtain more specific information about image features.Then convolutional layers with a 3×3 convolution kernel are superposed on those with a 1×1 convolutional kernel for multi-scale information fusion and improvement of network convergence speed.Also,the number of network layers is increased from four layers to seven layers to improve the matching accuracy.The parallax prediction is performed on the KITTI and Middlebury data sets.Experimental results show that,compared with the benchmark network,the proposed network structure shortens the convergence time by about 50.1% and reduces the matching error rate from 6.65% to 4.78%,achieving a smoother parallax effect in stereo matching.