Point-cloud-based　geometry　acquisition　techniques　have　seen　an　increased　deployment　in　recent　years　and　can　be　used　in　many　branches　of　science　and　engineering.　Different　from　conventional　computer　aided　design　(CAD)　models,　point-cloud　data　can　be　directly　obtained　by　applying　surveying　technology,　such　as　laser　scanning　devices.　This　opens　a　pathway　to　an　efficient　and　highly　automatic　acquisition　of　the　geometric　model.　However,　in　order　to　perform　numerical　analysis　on　point-cloud　models　using　conventional　finite　element　approaches,　a　surface　reconstruction　is　required,　which　is　both　time　consuming　and　error-prone.　Therefore,　a　direct　numerical　analysis　framework　based　on　point-cloud　data　in　conjunction　with　polyhedral　element　techniques　is　developed　in　this　contribution.　To　this　end,　a　robust　and　efficient　meshing　paradigm　based　on　the　direct　use　of　point-cloud　data　(without　surface　reconstruction)　is　proposed.　From　the　geometric　model,　a　trimmed　octree　mesh　containing　only　a　limited　number　of　master　elements　(element　patterns)　is　directly　generated,　which　is　highly　suitable　for　pre-computation　techniques　and　parallelization　(high-performance　computing–HPC).　By　computing　the　element　stiffness　and　mass　matrices　from　pre-computed　master　elements　via　scaling,　a　fully　automatic　HPC　framework　is　developed.　By　means　of　six　numerical　examples,　we　demonstrate　the　robustness,　versatility,　and　efficiency　of　the　developed　methodology　in　handling　(geometrically)　complex　engineering　problems.　©　2023　Elsevier　Ltd