The　6D　object　pose　obtained　from　single　RGB　image　has　broad　applications　such　as　robotic　manipulation　and　virtual　reality.　Among　many　existing　methods,　the　deep　learning-based　approaches　for　object　pose　estimation　from　single　RGB　image　are　widely　used.　However,　they　often　require　a　large　amount　of　training　data,　which　has　great　challenges　in　high　cost　of　data　collection　and　lack　of　3D　information.　In　this　paper,　we　introduce　an　object　pose　estimation　architecture　that　takes　a　single　RGB　image　as　input　and　directly　outputs　rotation　angles　and　translation　vectors.　A　data　generation　pipeline　that　applies　the　idea　of　domain　randomization　is　used　to　generate　millions　of　low-quality　rendering　images.　Then　the　pose　estimation　is　realized　by　fusing　the　architecture　and　the　domain　randomization　approach　to　utilize　the　generated　information　and　low　the　data　collection　cost.　We　synthesized　a　big　dataset　called　Pose6DDR　whose　images　are　similar　to　those　in　the　LineMod　dataset.　Experiments　demonstrated　the　effectiveness　of　the　proposed　6D　object　pose　estimation　architecture　as　compared　to　the　relevant　competing　technologies.