In　this　article,　we　propose　sampling-focused　marching　tree　(SMT)　to　guarantee　optimal　solutions　in　complex　environments　efficiently.　By　synergistically　integrating　heuristic　path　planning,　homotopy　space　computation,　and　adaptive　sampling　exploration,　SMT　swiftly　identifies　homotopic　solutions　to　the　optimal　solution　and　focuses　sampling　efforts　in　their　vicinity,　continually　refining　the　solution　to　efficiently　attain　the　global　optimum.　In　the　heuristic　path　planning　phase,　the　generalized　Voronoi　graph　feature　nodes　are　extracted　by　a　filter　to　facilitate　subsequent　computations.　Next,　the　feature　visibility　graph　is　constructed　based　on　the　feature　nodes　to　plan　a　heuristic　path.　In　the　homotopy　space　computation　phase,　feature　cell　decomposition　is　executed　using　the　feature　nodes　as　well　to　refine　the　obstacle-free　space.　Then,　the　homotopy　space　is　computed　by　examining　the　topological　connections　between　cells　and　the　heuristic　path　to　narrow　down　the　sampling　space.　In　the　adaptive　sampling　exploration　phase,　the　sampling　factor　is　adjusted　based　on　the　area　of　the　sampling　space　to　enhance　the　quality　of　samples.　After　adaptive　sampling　based　on　the　factor,　the　fast　marching　tree　is　leveraged　to　rapidly　explore　the　samples　and　find　the　optimal　solution.　A　thorough　analysis　of　SMT　is　provided,　including　completeness,　asymptotic　optimality,　and　computational　complexity.　Comprehensive　simulation　comparisons　with　current-leading　planning　approaches　in　complex　scenarios,　along　with　a　series　of　convincing　real-world　studies　have　been　conducted　to　provide　evidence　for　verifying　optimality　and　high-efficiency　computation　of　the　proposed　SMT.