Deciphering perceptual quality in colored point cloud: Prioritizing geometry or texture distortion?

Point cloud contents represent one of the prevalent formats for 3D representations. Distortions introduced at various stages in the point cloud processing pipeline affect the visual quality, altering their geometric composition, texture information, or both. Understanding and quantifying the impact of the distortion domain on visual quality is vital to driving rate optimization and guiding postprocessing steps to improve the overall quality of experience. In this paper, we propose a multi-task guided multi-modality no reference metric for measuring the quality of colored point clouds (M3-Unity), which utilizes 4 types of modalities across different attributes and dimensionalities to represent point clouds. An attention mechanism establishes inter/intra associations among 3D/2D patches, which can complement each other, yielding both local and global features, to fit the highly nonlinear property of the human vision system. A multi-task decoder involving distortion type classification selects the best combination among 4 modalities based on the specific distortion type, aiding the regression task and enabling the in-depth analysis of the interplay between geometrical and textural distortions. Furthermore, our framework design and attention strategy enable us to measure the impact of individual attributes and their combinations, providing insights into how these associations contribute particularly in relation to distortion type. Experimental results demonstrate that our method effectively predicts the visual quality of point clouds, achieving state-of-the-art performance on four benchmark datasets. The code will be released.

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