As an effective representation technol ogy for physical objects in three-dimensional (3D) space,3D colored point cloud (PC) can provide users with rich immersive visual experience.However,distortion s will be introduced in acquisition,processing,coding and transmission of 3D col ored PC,resulting in the decline of visual quality.Therefore,how to monitor the visual quality of 3D colored PC is an important problem to be solved.By projecti ng 3D colored PC onto two-dimensional planes,this paper proposes a novel visual quality assessment method for colored PC based on global and local perception f eatures.Firstly,3D colored PC is converted into color texture projection map (CT PM) and geometric projection map (GPM).Then,feature extraction is performed acco rding to different representations of texture and geometric distortions,includin g global color and local texture features based on CTPM,and global and local geo metry features based on GPM.Finally,all the global and local quality perception features constitute the final feature vector to predict the visual quality of co lored PC.Experimental results on two subjective quality assessment databases (SJ TU-PCQA,CPCD2.0) show that the performance of the proposed method is superior t o that of thirteen existing representative visual quality assessment methods,and it is more consistent with the subjective quality.