Layout　optimization　of　inspection　points　is　key　for　the　inspection　of　geometric　errors　in　models　with　complex　surfaces.　To　deal　with　difficulties　in　the　adaptive　planning　of　whole　inspection　points　of　model　components　that　were　characterized　by　multiple　surface　characteristics,　mesh-based　adaptive　planning　of　inspection　points　was　studied.　A　complex　surface　model　was　transformed　into　a　dense　triangular　mesh　model,　and　the　triangular　mesh　simplification　method　was　used　for　adaptive　planning　of　inspection　points.　The　local　polynomial　fitting　and　local　curvature　estimation　technology　were　used　for　adaptive　reduction　of　mesh　vertexes.　The　vertex　substitution　method　was　used　to　avoid　any　influence　from　the　meshing　discretization　error.　The　subset　selection　technology　was　used　to　improve　reduction　efficiency.　The　experimental　results　show　that　this　method　should　not　introduce　the　meshing　discretization　error　of　the　surface,　and　that　it　was　not　sensitive　to　meshing　granularity.　The　maximal　error　of　the　mesh-based　method　described　here　should　reduce　by　32.8%　under　the　same　detection　points　in　comparison　to　that　of　the　uniform　sampling　method,　and　should　reduce　by　16.9%　in　comparison　to　that　of　the　random　Hammersely　sequence　method.　The　mean　error　of　the　mesh-based　method　should　reduce　by　28.7%　and　18.5%　in　comparison　to　that　of　the　uniform　sampling　method　and　the　random　Hammersely　sequence　method,　respectively.　Meshing　can　avoid　coordination　difficulties　among　multiple　surfaces,　and　the　planned　detection　points　can　objectively　reflect　the　processing　quality　of　the　complex　surface.　©　2019,　Science　Press.　All　right　reserved.