6D　pose　estimation　is　widely　used　in　robot　tasks　such　as　sorting　and　grasping.　RGB-D-based　methods　have　recently　attained　brilliant　success,　but　they　are　still　susceptible　to　heavy　occlusion.　Our　critical　insight　is　that　color　and　geometry　information　in　RGBD　images　are　two　complementary　data,　and　the　crux　of　the　pose　estimation　problem　under　occlusion　is　fully　leveraging　them.　Towards　this　end,　we　propose　a　new　color　and　geometry　feature　fusion　module　that　can　efficiently　leverage　two　complementary　data　sources　from　RGB-D　images.　Unlike　prior　fusion　methods,　we　conduct　a　two-stage　fusion　strategy　to　do　color-depth　fusion　and　local-global　fusion　successively.　Specifically,　we　fuse　the　color　features　extracted　from　RGB　images　into　the　point　cloud　in　the　first　stage.　In　the　second　stage,　we　extract　local　and　global　features　from　the　fused　point　cloud　using　an　ASSANet-like　network　and　splice　them　together　to　obtain　the　final　fusion　features.　We　conducted　experiments　on　the　widely　used　LineMod　and　YCB-Video　datasets,　which　shows　that　our　method　improves　the　prediction　accuracy　while　reducing　the　training　time.　　©　2022　IEEE.