MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment

1 Sep 2022  ·  ZiCheng Zhang, Wei Sun, Xiongkuo Min, Quan Zhou, Jun He, Qiyuan Wang, Guangtao Zhai ·

The visual quality of point clouds has been greatly emphasized since the ever-increasing 3D vision applications are expected to provide cost-effective and high-quality experiences for users. Looking back on the development of point cloud quality assessment (PCQA) methods, the visual quality is usually evaluated by utilizing single-modal information, i.e., either extracted from the 2D projections or 3D point cloud. The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference point cloud quality assessment (NR-PCQA) metric in a multi-modal fashion. In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling. Then we render the point clouds into 2D image projections for texture feature extraction. To achieve the goals, the sub-models and projected images are encoded with point-based and image-based neural networks. Finally, symmetric cross-modal attention is employed to fuse multi-modal quality-aware information. Experimental results show that our approach outperforms all compared state-of-the-art methods and is far ahead of previous NR-PCQA methods, which highlights the effectiveness of the proposed method. The code is available at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Point Cloud Quality Assessment SJTU-PCQA MM-PCQA PLCC 0.92 # 1
RMSE 0.77 # 1
SROCC 0.91 # 1
KROCC 0.78 # 1
Point Cloud Quality Assessment WPC MM-PCQA PLCC 0.83 # 2
RMSE 12.84 # 2
SROCC 0.83 # 2
KROCC 0.64 # 1


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