We　present　a　novel　global　stereo　matching　approach　aimed　at　achieving　accurate　and　smooth　3D　label　disparity　estimation　based　on　LocalExp.　This　innovative　scheme　can　effectively　assign　an　appropriate　3D　label　to　each　pixel　from　an　infinite　label　space　and　generate　a　continuous　disparity　map　with　subpixel　accuracy.　To　address　the　persistent　challenge　of　weak　texture,　our　method　adopts　a　novel　approach　by　utilizing　calculated　initial　labels　rather　than　random　labels,　which　yields　enhanced　disparity　maps.　The　process　involves　employing　grid　cells　of　the　same　size　as　the　original　image　pair,　allowing　the　reduced　image　pair　to　capture　more　detailed　information　in　local　regions.　After　enlarging　the　disparity　map　of　reduced　image　pair　and　replacing　the　random　initial　labels　with　the　refined　ones,　we　achieve　superior　results　in　our　method.　Experimental　results　demonstrate　the　high　matching　accuracy　of　our　method　in　driving　scenes,　and　its　generalizability　is　verified.　©　2023　ACM.