MFE-Net: A Multi-Layer Feature Extraction Network for No-Reference Quality Assessment of 3-D Point Clouds

As an important data form, the point cloud (PC) can present real-world objects in a 3D and realistic way. In order to measure the performance of acquisition, processing, and compression systems, it is crucial to propose a PC quality assessment (PCQA) method. Nowadays, a lot of full-reference PCQA methods have been developed, but it is obviously difficult to have wide applications. Usually, we should complete PCQA tasks without the original PC, so a no-reference PCQA method based on deep learning is proposed in this paper, which is consisting of adaptive feature extraction (AFE) module, local quality acquisition (LQA) module, and global quality acquisition (GQA) module. Specifically, MFE-Net is the core of the LQA module that combines a feature fusion mechanism with hierarchical feature extraction module and regression module, and makes full use of different hierarchical features. Besides, in the AFE module, hand-crafted features are extracted from local sampling clusters of distorted PCs by an adaptive approach, including the distance from the sampling center point, mean curvature, and gray level of the point, to replace the PC as the input of MFE-Net. Afterward, deep features can be learned based on such hand-crafted features to predict the local quality score, and the global quality can be obtained through aggregation in the GQA module. Experimental results on the two publicly available subjective datasets, SJTU-PCQA and CPCD2.0 show that the performance of the proposed method is superior to state-of-the-art PCQA methods.