DA-Net: A Double Alignment Multimodal Learning Network for Point Cloud Quality Assessment

Existing multimodal point cloud quality assessment (PCQA) methods usually integrate 3D and 2D information to simulate human visual perception of distortions. However, due to the lack of consideration of spatial correspondence, they have difficulty to learn consistent distortion representations from different modalities in the same region of the PC. In addition, they also ignore the heterogeneity of modalities and rely on complex fusion mechanisms (e.g., attention) to integrate multimodal features. Both lead to limited performance and increased computational complexity. To address these limitations, we propose a novel double alignment multimodal learning network (DA-Net), which introduces two key alignment strategies. Specifically, the first is spatial pre-alignment strategy, which generates informative 2D patch for each 3D patch via an adaptive patch projection module (APPM), ensuring accurate spatial correspondence of different modalities prior to feature extraction. The second is a uniform feature alignment strategy, which includes feature disentanglement module (FDM) and feature mapping module (FMM) to relieve heterogeneity of modalities and guide the optimization of 2D and 3D encoder. Finally, multimodal features are simply integrated and regressed to obtain the quality score. Experimental results demonstrate that the DA-Net exhibits outstanding performance and generalization ability. It also achieves lower computational complexity compared with other multimodal PCQA methods. The source codes of DA-Net will be available at https://github.com/Rphone/DA-Net.