At　present,　three-dimensional　laser　scanners　are　used　to　scan　subway　shield　tunnels　and　generate　point　cloud　data　as　the　basis　for　extracting　a　variety　of　information　about　tunnel　defects.　However,　there　are　obstacles　in　the　tunnel　such　as　pipelines,　tracks,　and　signaling　systems　that　cause　noise　in　the　point　cloud.　Usually,　the　data　of　the　tunnel　point　cloud　are　huge,　and　the　efficiency　of　artificial　denoising　is　low.　Faced　with　this　problem,　based　on　the　respective　characteristics　of　the　geometric　shape　and　reflection　intensity　of　the　tunnel　point　cloud　and　their　correlation,　this　paper　proposes　a　tunnel　point　cloud　denoising　method.　The　method　includes　the　following　three　parts:　reflection　intensity　threshold　denoising,　joint　shape　and　reflection　intensity　denoising,　and　shape　denoising.　Through　the　experiment　on　the　single-ring　segment　point　cloud　of　a　shield　tunnel,　the　method　proposed　in　this　paper　takes　2　min　to　remove　99.77%　of　the　noise　in　the　point　cloud.　Compared　with　manual　denoising,　the　method　proposed　in　this　paper　takes　two　fifteenths　of　the　time　to　achieve　the　same　denoising　effect.　The　method　proposed　in　this　paper　meets　the　requirements　of　a　tunnel　point　cloud　data　survey.　Thus,　it　provides　support　for　the　efficient,　accurate,　and　automatic　daily　maintenance　and　surveys　of　tunnels.