MT-DPCQA: A Multimodal Time-aware Learning Approach for No-Reference Dynamic Point Cloud Quality Assessment

As the use of dynamic point clouds (DPCs) expands in immersive media settings including augmented and virtual reality, it has become more important than ever to have precise and scalable methods for quality evaluation. However, most existing objective Point Cloud Quality Assessment (PCQA) methods focus on static content and fail to capture the temporal dynamics and multimodal perceptual cues inherent in dynamic scenarios. In this work, we propose a no-reference dynamic PCQA framework that integrates both geometric and visual modalities with global temporal modeling for perceptually aligned quality prediction. For the 3D modality, we extract localized spatio-temporal features using a time-aware point cloud encoder that incorporates the normalized frame index as an additional input channel. In parallel, we generate two complementary projections per frame and extract visual features using a pre-trained convolutional network. A dynamic gating network adaptively weights the contributions of the two modalities at each time step. These weighted features are fused and passed to a temporal transformer, which captures long-range temporal dependencies to regress the final quality score. Comprehensive tests on benchmark datasets reveal that our approach surpasses existing full-reference and no-reference PCQA techniques, demonstrating its efficacy in assessing the quality of dynamic point clouds.