Category-level　6D　pose　estimation　aims　to　accurately　predict　the　spatial　position,　orientation　and　scale　of　unseen　objects　belonging　to　a　specific　category.　Existing　methods　often　fall　into　two　categories:　prior-based　approaches,　which　typically　utilize　the　Umeyama　algorithm　and　achieve　high　accuracy　but　suffer　from　training　limitations　and　computational　overhead,　and　end-to-end　methods,　which　offer　efficient　training　but　often　underperform　due　to　a　lack　of　category-specific　prior　knowledge.　To　bridge　this　gap,　we　propose　a　novel　framework　SCA-Pose　to　leverage　the　advantages　of　both　approaches.　SCA-Pose　consists　of　one　main　network　for　efficient　inference　and　an　auxiliary　network　for　enhanced　accuracy.　The　main　network,　featuring　local　and　global　feature　fusion　modules　(CNN　and　HS-Net)　and　a　trainable　pose　regressor,　enables　end-to-end　learning　for　real-time　applications.　The　auxiliary　network　further　refines　the　pose　prediction　by　incorporating　intrinsic　geometric　consistency　constraints　between　Normalized　Object　Coordinate　Space　(NOCS)　coordinates　and　object　pose　and　size,　while　adaptively　accounting　for　intra-class　shape　variations.　Experimental　results　on　the　REAL275　and　CAMERA25　datasets　show　that　SCA-Pose　has　significant　performance　improvement　compared　to　the　existing　baseline　method　(RBP-Pose)　and　can　achieve　real-time　operation　(30FPS).