Non-Local Geometry and Color Gradient Aggregation Graph Model for No-Reference Point Cloud Quality Assessment

No-Reference point cloud quality assessment (NR-PCQA) is a challenging task in computer vision due to the irregularity of point cloud structures and the unavailability of reference information. Existing point-based and projection-based NR-PCQA models are limited by the representation of point cloud distortion and the modeling of spatial topological structure. To address these limitations, we first propose two visual quality-related gradients: local-maximum geometry gradient and distance-weighted color gradient, which can effectively represent local variations in terms of spatial structure and color intensities between adjacent points. We further propose a non-local geometry and color gradient aggregation graph model for evaluating the perceptual quality of point clouds. Specifically, local graph convolutions are designed to model the topological relationship across neighboring points by aggregating the geometry and color gradients. Furthermore, a position-adaptive self-attention mechanism is introduced to expand the receptive field for modeling the global dependencies of point clouds. Experimental results on two benchmark databases demonstrate that the proposed model outperforms existing state-of-the-art methods.