As an effective description of 3D scenes and objects,color point clouds (CPCs) are widely used in many fields such as virtual reality and augmented reality.However,distortions will be introduced to CPC in the process of its collection, compression,transmission and reconstruction,so it is necessary to design an effective assessment method to evaluate the quality of distorted CPC.In this paper,a no-reference quality assessment method is proposed for CPC based on guided modulation.Considering the joint distortion of geometric information and color texture information,the guided modulation is used to combine them to comprehensively consider geometric distortion,color texture distortion and the joint distortion.Combined with the multi-channel characteristic of human eyes,Shearlet transform is used to extract features.Finally,the feature vector composed of all extracted features is inputted into the support vector regression (SVR) model to learn and predict the quality of point cloud.Experimental results show that the proposed method is well consistent with human subjective perception.