In order to overcome the ambiguity of ill-posed regions matching while enhancing the connectivity of dense disparity map

this paper proposes a binocular stereo matching method based on feature cascade convolutional neural network. We constructed a fully convolutional densely block with feature reuse to utilize the "skip-connection" mechanism to transmit the feature maps extracted from the previous layers to all subsequent layers

and compensated the local feature information of deep convolution losing. At the forward propagation stage

we designed an indicator function to divide a certain size of the training set as the input of the feature cascade convolutional network model

and applied the Mini-Batch Gradient Descent (MBGD) strategy to update the initial weight and bias parameters. We computed the initial matching cost according to the output of the presented network model

and used the Cross Based Cost Aggregation (CBCA) and Semi-Global Matching (SGM) pipeline to optimize the cost function for generating the accurate and dense disparity map. We used the published training and test sets of Middlebury database to evaluate our approach and other representative stereo matching methods

such as MC-CNN

CBMV and MC-CNN-WS. The experimental results prove that the proposed method has higher accuracy and better robustness of disparity estimation

and especially improves the matching rate and preserves image details in the regions of complex scene

illumination change and weakly textured.