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Cellular Aggregation Graph Convolutional Network for Point Cloud Quality Assessment
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AI
DOI：10.1109/TCSVT.2025.3624798.png)
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Abstract
En 中文
Point cloud quality assessment (PCQA) is a challenging task due to the inherently disordered nature of points. Existing point-based methods, such as sparse convolution and PointNet, are limited by local spatial modeling and structural feature extraction. Although 3D graph convolutional networks (GCNs) offer advantages in capturing local structural features through explicit geometric modeling and deformable kernels, their scalability is hindered by the high memory consumption associated with storing neighborhood matrices, particularly for large-scale point clouds. In this paper, to better extract hierarchical structural information and maintain efficiency in computational memory, we propose a novel point-based no-reference PCQA method, namely cellular aggregation network (CANet). The method effectively and efficiently extracts the quality-aware features of large patches in a divide-and-conquer manner. Specifically, a cellular sampling (CS) module is introduced to divide large patches into smaller cells, effectively avoiding the problem of memory explosion. A cellular aggregation (CA) module is proposed to extract intra-cell features and fuse inter-cell features. Moreover, a global aggregation (GA) module is presented to extract global sketch information. Finally, a long-term fusion (LTF) module is introduced to capture long-term dependencies between the features of the CA and GA modules. Experimental results on benchmark datasets demonstrate that the proposed model achieves state-of-the-art performance.
Keywords:
Point cloud quality assessment
no-reference
point-based
graph convolutional network
Journal
IF:
11.1
Papers:
873
Citations:
3.1W
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