MFE-Net: A Multi-Layer Feature Extraction Network for No-Reference Quality Assessment of 3-D Point Clouds
MFE-Net: A Multi-Layer Feature Extraction Network for No-Reference Quality Assessment of 3-D Point Clouds
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DOI：
10.1109/tbc.2023.3311339
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发表时间：
2024-03
影响因子：
4.5
通讯作者：
Qihao Liang;Zhouyan He;Mei Yu;Ting Luo;Haiyong Xu
中科院分区：
文献类型：
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作者：
Qihao Liang;Zhouyan He;Mei Yu;Ting Luo;Haiyong Xu
点云作为一种重要的数据形式，能够以三维、逼真的方式表示现实世界中的物体。为了衡量采集、处理和压缩系统的性能，提出一种PC质量评估（PCQA）方法至关重要。目前，已经开发出了许多全参考PCQA方法，但显然难以得到广泛的应用。通常情况下，PCQA任务需要在没有原始PC的情况下完成，为此，提出了一种基于深度学习的无参考PCQA方法，该方法由自适应特征提取（AFE）模块、局部质量获取（LQA）模块和全局质量获取（GQA）模块组成。具体来说，MFE-Net是LQA模块的核心，它将特征融合机制与层次特征提取模块和回归模块相结合，充分利用了不同层次的特征。此外，在AFE模块中，通过自适应方法从畸变PC的局部采样簇中提取手工特征，包括到采样中心点的距离、平均曲率和点的灰度，以代替PC作为MFE-Net的输入。然后，可以基于这些手工制作的特征来学习深度特征以预测局部质量分数，并且可以通过GQA模块中的聚合来获得全局质量。在SJTU-PCQA和CPCD 2. 0两个公开的主观数据集上的实验结果表明，该方法的性能上级现有的PCQA方法.
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.