Multi-view aggregation transformer for no-reference point cloud quality assessment

With the increasing maturity of 3D point cloud acquisition, storage, and transmission technologies, a large number of distorted point clouds without original reference exist in practical applications. Hence, it is necessary to design a no-reference point cloud quality assessment (PCQA) for point cloud systems. However, the existing no-reference PCQA metrics ignore the content differences and positional context among the projected images. For this, we propose a Multi-View Aggregation Transformer (MVAT) with two different fusion modules to extract the comprehensive feature representation of PCQA. Specifically, considering the content differences of different projected images, we first design a Content Fusion Module (CFM) to fuse multiple projected image features by adaptive weighting. Then, we design a Bidirectional Context Fusion Module (BCFM) to extract context features for reflecting the contextual relationship among projected images. Finally, we joint the above two fusion modules via Content-Position Fusion Module (CPFM) to fully mine the feature representation of point clouds. Experimental results show that our MVAT can achieve comparable or better performance than state-of-the-art metrics on three open point cloud datasets.