The visual quality of point clouds is critical for perception-centric immersive media. Point Cloud Quality Assessment (PCQA) is crucial for reducing costs associated with human evaluation, optimizing compression pipeline and enhancing human visual perception. However, real-valued PCQA methods often struggle to capture the coupled geometric and perceptual cues that govern quality. Com-PCQA, a novel no-reference PCQA framework leveraging complex-valued feature learning, is proposed. First, a Hilbert dual-stream module transforms multi-modal inputs of point clouds and images into analytic signals in the complex domain, enabling joint modeling of global structure and local texture with efficient tensor operations. Second, a complex amplitude-phase attention (CAPA) module explicitly decomposes and fuses amplitude features that describe geometric structure and phase features that capture fine-grained details, and it can be seamlessly integrated into other PCQA frameworks to enhance performance. Third, an adversarial joint scoring module integrates adversarial training with collaborative learning to calibrate multi-modal, multi-scale representations and enhance robustness. Extensive experiments on three public databases show that Com-PCQA achieves state-of-the-art correlations with subjective scores and consistently outperforms recent PCQA methods, demonstrating its effectiveness and robustness. The code will be available at https://openi.pcl.ac.cn/OpenPointCloud and https://github.com/LareinaSu/Com-PCQA.
Su et al. (2026) studied this question.