Revisit Point Cloud Quality Assessment: Current Advances and a Multiscale-Inspired Approach.

Abstract

The demand for full-reference point cloud quality assessment (PCQA) has extended across various point cloud services. Unlike image quality assessment, where the reference and the distorted images are naturally aligned in coordinates and thus allow point-to-point (P2P) color assessment, the coordinates and attributes of a 3D point cloud may both suffer from distortion, making the P2P evaluation unsuitable. To address this, PCQA methods usually define a set of key points and construct a neighborhood around each key point for neighbor-to-neighbor (N2N) computation on geometry and attribute. However, state-of-the-art PCQA methods often exhibit limitations in certain scenarios due to insufficient consideration of key points and neighborhoods. To overcome these challenges, this paper proposes PQI, a simple yet efficient metric to index point cloud quality. PQI suggests using scale-wise key points to uniformly perceive distortions within a point cloud, along with a mild neighborhood size associated with each key point for compromised N2N computation. To achieve this, PQI employs a multiscale framework to obtain key points, ensuring comprehensive feature representation and distortion detection throughout the entire point cloud. Such a multiscale method merges every eight points into one in the downsampling processing, implicitly embedding neighborhood information into a single point and thereby eliminating the need for an explicitly large neighborhood. Further, within each neighborhood, simple features, such as geometry Euclidean distance difference and attribute value difference, are extracted. Feature similarity is then calculated between the reference and the distorted samples at each scale and linearly weighted to generate the final PQI score. Extensive experiments demonstrate the superiority of PQI, consistently achieving high performance across several widely recognized PCQA datasets. Moreover, PQI is highly appealing for practical applications due to its low complexity and flexible scale options.