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To improve the viewer's Quality of Experience (QoE) and optimize computergraphics applications, 3D model quality assessment (3D-QA) has become animportant task in the multimedia area. Point cloud and mesh are the two mostwidely used digital representation formats of 3D models, the visual quality ofwhich is quite sensitive to lossy operations like simplification andcompression. Therefore, many related studies such as point cloud qualityassessment (PCQA) and mesh quality assessment (MQA) have been carried out tomeasure the visual quality degradations of 3D models. However, a large part ofprevious studies utilize full-reference (FR) metrics, which indicates they cannot predict the quality level with the absence of the reference 3D model.Furthermore, few 3D-QA metrics consider color information, which significantlyrestricts their effectiveness and scope of application. In this paper, wepropose a no-reference (NR) quality assessment metric for colored 3D modelsrepresented by both point cloud and mesh. First, we project the 3D models from3D space into quality-related geometry and color feature domains. Then, the 3Dnatural scene statistics (3D-NSS) and entropy are utilized to extractquality-aware features. Finally, machine learning is employed to regress thequality-aware features into visual quality scores. Our method is validated onthe colored point cloud quality assessment database (SJTU-PCQA), the Waterloopoint cloud assessment database (WPC), and the colored mesh quality assessmentdatabase (CMDM). The experimental results show that the proposed methodoutperforms most compared NR 3D-QA metrics with competitive computationalresources and greatly reduces the performance gap with the state-of-the-art FR3D-QA metrics. The code of the proposed model is publicly available now tofacilitate further research.

No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models