Local and Global Structure-Guided No-Reference Point Cloud Quality Assessment

As a crucial representation of 3D data, a point cloud (PC) can accurately capture the geometry, structure, and color information of objects. However, various quality problems arise owing to device noise, data acquisition errors, and compression algorithms, limiting the application of PCs. Therefore, assessing PC quality to determine its suitability for applications is a challenging task. In this work, a local and global structure-guided feature extraction and attention network (LGS-Net) is introduced for no-reference PC quality assessment (PCQA). This approach incorporates cluster construction (CC), local structure-guided cluster feature extraction (LSFE), and global structure-guided attention (GSA) modules. First, owing to the heightened sensitivity of the human visual system (HVS) to structural information, a graph filter is employed to identify high-frequency clusters. Within the LSFE module, a multiscale strategy is employed to ensure that structural information effectively influences both the geometry and color information. Simultaneously, the multiscale features within the cluster are dynamically fine-tuned using feature channel weight reassignment. To account for the impact of interclusters on overall quality, a GSA module is introduced to establish global dependencies between local clusters. This approach enables the extraction of final geometry, color, and structure information, which are ultimately used for accurate quality assessment. Extensive experimental results show that the proposed method outperforms the existing state-of-the-art PCQA methods using two publicly available subjective datasets.