The　expansion　of　tunnel　scale　has　led　to　a　massive　demand　for　inspections.　Light　Detection　And　Ranging　(LiDAR)　has　been　widely　applied　in　tunnel　structure　inspections　due　to　its　fast　data　acquisition　speed　and　strong　environmental　adaptability.　However,　raw　tunnel　point-cloud　data　contain　noise　point　clouds,　such　as　non-structural　facilities,　which　affect　the　detection　of　tunnel　lining　structures.　Methods　such　as　point-cloud　filtering　and　machine　learning　have　been　applied　to　tunnel　point-cloud　denoising,　but　these　methods　usually　require　a　lot　of　manual　data　preprocessing.　In　order　to　directly　denoise　the　tunnel　point　cloud　without　preprocessing,　this　study　proposes　a　comprehensive　processing　method　for　cross-section　fitting　and　point-cloud　denoising　in　subway　shield　tunnels　based　on　the　Huber　loss　function.　The　proposed　method　is　compared　with　classical　fitting　denoising　methods　such　as　the　least-squares　method　and　random　sample　consensus　(RANSAC).　This　study　is　experimentally　verified　with　40　m　long　shield-tunnel　point-cloud　data.　Experimental　results　show　that　the　method　proposed　in　this　study　can　more　accurately　fit　the　geometric　parameters　of　the　tunnel　lining　structure　and　denoise　the　point-cloud　data,　achieving　a　better　denoising　effect.　Meanwhile,　since　coordinate　system　transformations　are　required　during　the　point-cloud　denoising　process　to　handle　the　data,　manual　rotations　of　the　coordinate　system　can　introduce　errors.　This　study　simultaneously　combines　the　Huber　loss　function　with　principal　component　analysis　(PCA)　and　proposes　a　three-dimensional　spatial　coordinate　system　transformation　method　for　tunnel　point-cloud　data　based　on　the　characteristics　of　data　distribution.