Balancing Representation Abstractions and Local Details Preservation for 3d Point Cloud Quality Assessment

3D Point Clouds (PCs) have become a valuable tool for representing intricate 3D information. Assessing the quality of PCs remains a challenging task, especially when striving for optimal immersive experiences. This paper introduces a novel metric and training approach that leverages projection-based views to evaluate the quality of 3D content. Our approach addresses a critical issue related to the intrinsic bias of deep networks for image recognition towards building hierarchical representations including only the global semantic, at the expense of local details. This bias is a limiting factor in tasks like 3D point cloud quality assessment where instances of the same content with varying degrees and types of degradation can possess strikingly similar representations. We propose a novel point cloud quality metric using a dual supervised and unsupervised training strategy to balance semantic understanding and preservation of critical perceptual quality-relevant information. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics on two standard 3D PCs quality assessment benchmarks (3D PCQA).