Point Cloud Projection and Multi-Scale Feature Fusion Network Based Blind Quality Assessment for Colored Point Clouds

With the wide applications of colored point cloud (CPC) in many fields, many attentions have been paid to CPC's distortions caused by its compression and reconstruction. How to effectively evaluate the visual quality of CPC has become an urgent issue to be resolved. In this paper, a Point cloud projection and Multi-scale feature fusion network based Blind Visual Quality Assessment method (denoted as PM-BVQA) is proposed for CPC. CPC in 3D space is first projected into 2D color projection map and geometric projection map, then a multi-scale feature fusion network is designed to blindly evaluate the visual quality of CPC. The proposed PM-BVQA method includes three modules, that is, joint color-geometric feature extractor, two-stage multi-scale feature fusion, and spatial pooling module. Considering the multi-channel characteristics of human visual system (HVS), unimodal features of different scales are obtained by joint color-geometric feature extractor from the color and geometric projection maps. The fusion of the unimodal color and geometric features is carried out to capture the cross-modal complementary information between these two types of information. By integrating cross-modal fused features at different scales, the complementary relationships between different channels of HVS are simulated. The spatial pooling module takes into account the attention mechanism of HVS and realizes the weighted summation of local regional quality to obtain the final global quality score of CPC. A subjective CPC database with coding distortion is used to verify the effectiveness of the proposed method, and the experimental results show that the proposed blind quality assessment method is more consistent with the subjective visual perception than the existing quality assessment methods.