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收稿：2023-09-28，
修回：2023-12-15，
纸质出版：2024-10-16
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目的
2
针对现有无参考点云质量评估方法需要将点云预处理为二维投影或其他形式导致引入额外噪声、限制空间上下文等问题，提出了一种基于邻域信息嵌入变换模块和点云级联注意力模块的无参考点云质量评估方法。
方法
2
将点云样本整体作为输入，减轻预处理引入的失真。使用稀疏卷积搭建U型主干网络提取多尺度特征，邻域信息嵌入变换模块逐点学习提取特征，点云级联注意力模块增强小尺度特征，提高特征信息的可辨识性，最后逐步聚合多尺度特征信息形成特征向量，经全局自适应池化和回归函数进行回归预测，得到失真点云质量分数。
结果
2
实验在2个数据集上与现有的12种代表性点云质量评估方法进行了比较，在SJTU-PCQA（Shanghai Jiao Tong University subjective point cloud quality assessment）数据集中，相比于性能第2的模型，PLCC（Pearson linear correlation coefficient）值提高了8.7%，SROCC（Spearman rank-order coefficient correlation）值提高了0.39%；在WPC（waterloo point cloud）数据集中，相比于性能第2的模型，PLCC值提高了4.9%，SROCC值提高了3.0%。
结论
2
所提出的基于邻域信息嵌入变换和级联注意力的无参考点云质量评估方法，提高了可辨识特征提取能力，使点云质量评估结果更加准确。
Objective
2
This study introduces a novel method that aims to address the shortcomings of current no-reference point cloud quality assessment methods. Such methods necessitate the preprocessing of point clouds into 2D projections or other forms， which may introduce additional noise and limit the spatial contextual information of the data. The proposed approach overcomes these limitations. The approach comprises two crucial components， namely， the neighborhood information embedding transformation module and the point cloud cascading attention module. The former module is intended to capture the point cloud data’s local features and geometric structure without any extra preprocessing. This process preserves the point cloud’s original information and minimizes the potential for introducing additional noise， all while providing a more expansive spatial context. The latter module enhances the precision and flexibility of point cloud quality assessment by merging spatial and channel attention. The module dynamically learns weightings and applies them to features based on the aspects of various point cloud data， resulting in a more comprehensive understanding of multidimensional point cloud information.
Method
2
The proposed model employs innovative strategies to address challenges in assessing point cloud quality. In contrast to traditional approaches， it takes the original point cloud sample as input and eliminates the need for preprocessing. This process helps maintain the point cloud’s integrity and improve accuracy in assessment. Second， a U-shaped backbone network is constructed using sparse convolution to enable multiscale feature extraction， allowing the model to capture different scale features of the point cloud and understand point cloud data at local and overall levels more effectively. The module for transforming neighborhood information embedding is an essential part of the process because it extracts features through point-by-point learning. This process assists the model in thoroughly comprehending the local information present in the point cloud. Furthermore， the attention module for point cloud cascade bolsters small-scale features， elevating the recognizability of feature information. By progressively consolidating the multiscale feature information to construct a feature vector， the model can thoroughly represent the quality features of the point cloud. Ultimately， global adaptive pooling and regression functions are employed for regression prediction to finally obtain quality scores for distorted point clouds. The model’s architecture utilizes multiscale information to improve the representation and evaluation of features， resulting in increased progress and efficiency in the assessment of point cloud quality.
Result
2
In this study， a set of experiments was conducted to validate the efficacy of the proposed method for assessing the quality of point clouds. The results of the experiments demonstrate that the method shows substantial enhancements over 12 existing representative assessment methods for point cloud quality on two different datasets. The experiment specifically employs the SJTU-PCQA dataset， and the novel technique enhances the PLCC value by 8.7% and the SROCC value by 0.39% relative to the model with the second-highest performance. Thus， the new method more precisely evaluates the point cloud quality on the SJTU-PCQA dataset with improved correlation and performance. Similarly， the novel approach enhances the PLCC metric by 4.9% and the SROCC metric by 3.0% on the WPC dataset， surpassing the model with the second-best results. This result illustrates the efficiency and effectiveness of the new approach in point cloud quality assessment for various datasets. The results of these experiments highlight the effectiveness and superiority of the method proposed， providing substantial backing for subsequent research and applications in the arena of point cloud quality assessment. Furthermore， the method showcases its widely applicable nature.
Conclusion
2
The no-reference method presented in this study enhances the precision of point cloud quality assessment. The technique employs a novel structure of embedding transformation for neighborhood information and cascading attention. Emphasis is placed on recognizable feature extraction to yield more accurate results in point cloud quality assessment.
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