Although　advancements　in　red-green-blue-depth　(RGB-D)-based　six　degree-of-freedom　(6D)　pose　estimation　methods,　severe　occlusion　remains　challenging.　Addressing　this　issue,　we　propose　a　novel　feature　fusion　module　that　can　efficiently　leverage　the　color　and　geometry　information　in　RGB-D　images.　Unlike　prior　fusion　methods,　our　method　employs　a　two-stage　fusion　process.　Initially,　we　extract　color　features　from　RGB　images　and　integrate　them　into　a　point　cloud.　Subsequently,　an　anisotropic　separable　set　abstraction　network-like　network　is　utilized　to　process　the　fused　point　cloud,　extracting　both　local　and　global　features,　which　are　then　combined　to　generate　the　final　fusion　features.　Furthermore,　we　introduce　a　lightweight　color　feature　extraction　network　to　reduce　model　complexity.　Extensive　experiments　conducted　on　the　LineMOD,　Occlusion　LineMOD,　and　YCB-Video　datasets　conclusively　demonstrate　that　our　method　significantly　enhances　prediction　accuracy,　reduces　training　time,　and　exhibits　robustness　to　occlusion.　Further　experiments　show　that　our　model　is　significantly　smaller　than　the　latest　popular　6D　pose　estimation　models,　which　indicates　that　our　model　is　easier　to　deploy　on　mobile　platforms.