Non-local　similarity　(NLS)　has　been　successfully　applied　to　point　cloud　denoising.　However,　existing　non-local　methods　either　involve　high　algorithmic　complexity　in　capturing　NLS　or　suffer　from　diminished　accuracy　in　estimating　low-rank　matrices.　To　address　these　problems,　we　propose　a　Point　Cloud　Denoising　framework　using　\gamma-norm　minimization　based　on　Curvature　Entropy　(PCD-\gammaCE)　for　efficiently　removing　noise.　First,　we　develop　a　structure　descriptor,　which　exploits　Curvature　Entropy　(CE)　to　accurately　capture　shape　variation　details　of　Non-Local　Similar　Structure　(NLSS),　and　employs　Angle　Subdivision　(AS)　of　NLSS　to　control　the　complexity　of　initial　normal　matrix　construction.　Second,　we　introduce　\gamma-norm　to　construct　a　low-rank　denoising　model　for　initial　normal　matrix,　thereby　providing　a　nearly　unbiased　estimation　of　rank　function　with　better　robustness　to　noise.　Extensive　experiments　on　synthetic　and　raw　scanned　point　clouds　show　that　our　approach　outperforms　the　popular　denoising　methods,　with　a　99.90%　time　reduction　and　gains　in　Mean　Square　Error　(MSE)　and　Chamfer　Distance　(CD)　compared　with　the　Weighted　Nuclear　Norm　Minimization　(WNNM)　method.　The　code　will　be　available　soon　at　https://github.com/fancj2017/PCD-rCE.　©　1995-2012　IEEE.