According　to　the　discontinuity　of　the　threedimensional　rotation　representation　and　the　singleness　of　pose　estimation　method　in　the　ES6D　pose　estimation　network,　this　paper　designs　a　dual　decoder　to　replace　the　decoder　in　the　original　ES6D.　First,　this　paper　make　lightweight　improvements　to　ES6D＇s　decoder　as　an　explicit　decoder,　and　use　six-dimensional(6D)　rotation　coefficients　to　replace　quaternions　to　represent　three-dimensional(3D)　rotations.　Then,　this　paper　designs　an　implicit　decoder　with　a　different　working　mechanism　to　assist　the　training　of　the　supervised　explicit　encoder.　Finally,　this　paper　performs　fine-grained　learning　by　freezing　the　parameters　of　decoders　to　enhance　pose　consistency.　Experiments　show　that　the　parameters　and　calculations　of　the　improved　decoder　in　the　inference　stage　are　reduced　by　33.2%　and　33.4%　respectively,　the　inference　speed　is　increased　by　20.4%,　and　the　accuracy　on　the　mainstream　data　sets　LineMOD　and　YCB-Video　is　increased　by　1.0%　and　0.6%　respectively,　and　the　convergence　speed　is　significantly　improved.　©　2023　IEEE.