Some　robust　point　cloud　registration　approaches　with　controllable　pose　refinement　magnitude,　such　as　ICP　and　its　variants,　are　commonly　used　to　improve　6D　pose　estimation　accuracy.　However,　the　effectiveness　of　these　methods　gradually　diminishes　with　the　advancement　of　deep　learning　techniques　and　the　enhancement　of　initial　pose　accuracy,　primarily　due　to　their　lack　of　specific　design　for　pose　refinement.　In　this　paper,　we　propose　Point　Cloud　Completion　and　Keypoint　Refinement　with　Fusion　Data　(PCKRF),　a　new　pose　refinement　pipeline　for　6D　pose　estimation.　The　pipeline　consists　of　two　steps.　First,　it　completes　the　input　point　clouds　via　a　novel　pose-sensitive　point　completion　network.　The　network　uses　both　local　and　global　features　with　pose　information　during　point　completion.　Then,　it　registers　the　completed　object　point　cloud　with　the　corresponding　target　point　cloud　by　our　proposed　Color　supported　Iterative　KeyPoint　(CIKP)　method.　The　CIKP　method　introduces　color　information　into　registration　and　registers　a　point　cloud　around　each　keypoint　to　increase　stability.　The　PCKRF　pipeline　can　be　integrated　with　existing　popular　6D　pose　estimation　methods,　such　as　the　full　flow　bidirectional　fusion　network,　to　further　improve　their　pose　estimation　accuracy.　Experiments　demonstrate　that　our　method　exhibits　superior　stability　compared　to　existing　approaches　when　optimizing　initial　poses　with　relatively　high　precision.　Notably,　the　results　indicate　that　our　method　effectively　complements　most　existing　pose　estimation　techniques,　leading　to　improved　performance　in　most　cases.　Furthermore,　our　method　achieves　promising　results　even　in　challenging　scenarios　involving　textureless　and　symmetrical　objects.　Our　source　code　is　available　at　https://github.com/zhanhz/KRF.　IEEE