Structural　analysis　of　small-scale　objects　holds　paramount　significance　in　fields　such　as　industrial　manufacturing　and　medical　testing.　Currently,　3D　reconstruction　method　based　on　shape　from　focus　(SFF)　has　emerged　as　an　efficacious　approach　for　acquiring　submicron-level　height　change　information.　However,　in　the　reconstruction　of　a　broader　range　of　objects　with　small　heights,　the　conflicting　demands　of　SFF　for　thin　depth-of-field　and　largefield　present　new　challenges.　Traditional　single-field　SFF(SF-SFF)　reconstruction　methods　are　limited　to　obtaining　tiny　local　information　of　objects,　and　existing　SFF　methods　involve　a　trade-off　between　reconstruction　speed　and　accuracy.　Therefore,　we　propose　a　novel　multi-field　SFF(MF-SFF)　framework.　This　framework　incorporates　pulse　trigger　acquisition　control　and　parallel　chain　processing　(PCP)　strategy,　effectively　addressing　challenges　associated　with　small-scale　objects.　It　overcomes　issues　related　to　the　rapid　collection　and　processing　of　massive　image　data　during　the　3D　reconstruction　of　objects.　Through　experimental　validation,　the　MF-SFF　framework　not　only　accomplishes　high-precision　and　rapid　3D　reconstruction　but　also　demonstrates　robust　adaptability.　In　contrast　to　traditional　methods,　the　MF-SFF　framework　breaks　through　the　limitations　of　the　collection　range,　ensuring　reconstruction　accuracy　while　addressing　challenges　associated　with　reconstruction　speed.　It　offers　an　efficient　and　feasible　solution　for　the　3D　structural　analysis　of　small-scale　objects,　introducing　new　possibilities　for　research　and　applications　in　related　fields.