Estimating　depth/disparity　information　from　stereo　pairs　via　stereo　matching　is　a　classical　research　topic　in　computer　vision.　Recently,　along　with　the　development　of　deep　learning　technologies,　many　end-to-end　deep　networks　have　been　proposed　for　stereo　matching.　These　networks　generally　borrow　convolutional　neural　network　(CNN)　structures　originally　designed　for　other　tasks　to　extract　features.　These　structures　are　generally　redundant　for　the　task　of　stereo　matching.　Besides,　3D　convolutions　in　these　networks　are　too　complex　to　be　extended　for　large　perception　fields　which　are　helpful　for　disparity　estimation.　In　order　to　overcome　these　problems,　we　propose　a　deep　network　structure　based　on　the　properties　of　stereo　matching.　In　the　proposed　network,　a　concise　and　effective　feature　extraction　module　is　presented.　Moreover,　a　separated　3D　convolution　is　introduced　to　avoid　parameter　explosion　caused　by　increasing　the　size　of　convolution　kernels.　We　validate　our　network　on　the　dataset　of　SceneFlow　in　aspects　of　both　accuracy　and　computation　costs.　Results　show　that　the　proposed　network　obtains　state-of-the-art　performance.　Compared　with　the　other　structures,　our　feature　extraction　module　can　reduce　90%　parameters　and　25%　time　cost　while　achieving　comparable　accuracy.　At　the　same　time,　our　separated　3D　convolution,　accompanied　by　group　normalization　(GN),　achieves　lower　end-point-error　(EPE)　than　baseline　methods.　©　2020,　Science　Press.　All　right　reserved.