The　6D　object　pose　obtained　from　single　RGB　image　has　broad　applications　such　as　robotic　manipulation　and　virtual　reality.　However,　the　deep　learning-based　pose　estimation　methods　usually　require　a　large　amount　of　trainging　data　to　improve　the　generalization　ability　of　the　model,　and　in　general,　the　common　data　generation　methods　have　great　challenges　in　high　cost　of　data　collection　and　lack　of　3D　information.　This　paper　proposes　a　6D　object　pose　estimation　network　with　low-quality　rendering　images.　In　this　network,　the　feature　extraction　part　takes　a　single　RGB　image　as　the　input　of　the　network,　and　uses　the　residual　network　to　extract　the　features　of　this　image.　The　classification　stream　of　the　pose　estimation　part　predicts　the　category　of　the　target　object,　and　the　regression　stream　returns　the　rotation　angle　and　translation　vector　of　the　target　object　in　3D　space.　Moreover,　the　domain　randomization　method　is　used　to　establish　a　large-scale　low-quality　rendering　images　with　the　3D　spatial　position　information　in　a　low　collection　cost.　The　experimental　results　on　the　established　Pose6DDR　dataset　and　the　public　LineMod　dataset　verify　the　superiority　of　the　proposed　pose　estimation　method　and　the　effectiveness　of　the　established　large-scale　simulation　dataset.　©　2022,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.