Adaptive Geometric Attention-Driven No-Reference Multi-Modal Point Cloud Quality Assessment

Point clouds play an essential role in 3D visual media applications. Point Cloud Quality Assessment (PCQA) is vital to improving the subjective experience, but lacking an effective geometric characterization hinders its consistency with subjective perception. To tackle this problem, this paper introduces a No-Reference Multi-Modal Point Cloud Quality Assessment (NR-PCQA) approach driven by adaptive geometric attention. Specifically, this paper defines two dimensionless geometric descriptors, namely, radial depth ratio (RDR) and relative radial distance (RRD). These two descriptors are then used to construct an adaptive geometric attention mechanism (AGAM), which dynamically guides the network to focus on features related to geometric quality during feature extraction. Based on AGAM, a multi-modal fusion framework is further built, where the three-dimensional geometric cues are combined with the texture and semantic information of the two-dimensional projection images, leading to accurate point cloud quality assessment. Creatively, the Hierarchical Multi-Modal Attention Fusion (HMAF) mechanism is designed to achieve the complementary strengths of 3D and 2D features, which firstly maximizes the extraction of single-modal features and then deeply merges cross-modal information. Naturally, the experimental results on SJTU-PCQA and WPC databases demonstrate that the innovative design of AGAM and HMAF achieves effective multi-modal feature fusion on the basis of satisfactory description for geometric properties, resulting in higher subjective consistency than the state-of-the-art methods. Meanwhile, the proposed method exhibits strong robustness across various types of point cloud distortions and diverse point cloud content, providing comprehensive validation of its effectiveness and practicality.