No-Reference Point Cloud Quality Assessment via Graph Convolutional Network

• URL: http://arxiv.org/abs/2411.07728v1
• Date: Tue, 12 Nov 2024 11:39:05 GMT
• Title: No-Reference Point Cloud Quality Assessment via Graph Convolutional Network
• Authors: Wu Chen, Qiuping Jiang, Wei Zhou, Feng Shao, Guangtao Zhai, Weisi Lin,
• Abstract summary: Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers. Point clouds inevitably suffer from quality degradation and information loss through multimedia communication systems. We propose a novel no-reference PCQA method by using a graph convolutional network (GCN) to characterize the mutual dependencies of multi-view 2D projected image contents.
• Score: 89.12589881881082
• License:
• Abstract: Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers as it can provide more realistic visual information than two-dimensional (2D) data. Similar to 2D plane images and videos, point clouds inevitably suffer from quality degradation and information loss through multimedia communication systems. Therefore, automatic point cloud quality assessment (PCQA) is of critical importance. In this work, we propose a novel no-reference PCQA method by using a graph convolutional network (GCN) to characterize the mutual dependencies of multi-view 2D projected image contents. The proposed GCN-based PCQA (GC-PCQA) method contains three modules, i.e., multi-view projection, graph construction, and GCN-based quality prediction. First, multi-view projection is performed on the test point cloud to obtain a set of horizontally and vertically projected images. Then, a perception-consistent graph is constructed based on the spatial relations among different projected images. Finally, reasoning on the constructed graph is performed by GCN to characterize the mutual dependencies and interactions between different projected images, and aggregate feature information of multi-view projected images for final quality prediction. Experimental results on two publicly available benchmark databases show that our proposed GC-PCQA can achieve superior performance than state-of-the-art quality assessment metrics. The code will be available at: https://github.com/chenwuwq/GC-PCQA.

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