Shape　from　focus　(SFF)　is　a　method　of　recovering　the　three-dimensional　shape　of　objects　from　the　image　sequence　through　various　focusing　measurement　operators,　and　is　widely　used　in　the　fields　of　medical　diagnosis,　PCB　inspection,　industrial　manufacturing,　and　robotic　operations.　In　SFF,　the　wrong　focus　measurement　causes　an　inaccurate　reconstruction　of　the　object.　Generally,　to　improve　the　accuracy　of　focusing　measurements,　many　focus　measure　(FM)　operators　are　proposed,　which　is　a　method　that　improves　the　focus　volume　(FV)　perfor-mance.　However,　due　to　the　influence　of　blur　diffusion,　which　refers　to　the　overlapping　of　blur　circles　between　different　regions,　the　calculation　results　of　the　FM　operator　in　the　local　window　often　do　not　meet　the　stability　requirements　of　complex　scenarios.　It　is　difficult　to　provide　high-precision　reconstruction　results　in　practical　applications.　To　overcome　this　limitation,　we　proposed　an　SFF　framework　based　on　block　processing　and　heat　-diffusion　refinement　to　obtain　a　refined　depth　map　(RDM).　The　acquisition　of　an　RDM　is　mainly　divided　into　two　steps.　First,　the　image　is　divided　into　blocks　to　reduce　the　effect　of　blur　diffusion,　and　the　FM　operator　is　used　to　calculate　the　focus　measurement　value　of　each　block　to　form　the　FV.　The　initial　depth　map　(IDM)　is　obtained　by　searching　for　the　best　focus　position　in　the　FV.　The　second　step　is　to　refine　the　IDM　based　on　the　heat-diffusion　principle　to　obtain　the　best　focus　position　for　all　pixels　in　each　block　and　derive　the　RDM.　The　results　of　ex-periments　conducted　on　synthetic　and　real　objects　demonstrate　the　superiority　of　our　method　in　terms　of　ac-curacy　and　its　high　stability　in　different　application　scenarios.