  We present a novel no-reference quality assessment metric, the image
transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For
quality assessment, deep neural network (DNN) has shown compelling performance
on no-reference metric design. However, the most challenging issue for
no-reference PCQA is that we lack large-scale subjective databases to drive
robust networks. Our motivation is that the human visual system (HVS) is the
decision-maker regardless of the type of media for quality assessment.
Leveraging the rich subjective scores of the natural images, we can quest the
evaluation criteria of human perception via DNN and transfer the capability of
prediction to 3D point clouds. In particular, we treat natural images as the
source domain and point clouds as the target domain, and infer point cloud
quality via unsupervised adversarial domain adaptation. To extract effective
latent features and minimize the domain discrepancy, we propose a hierarchical
feature encoder and a conditional-discriminative network. Considering that the
ultimate purpose is regressing objective score, we introduce a novel
conditional cross entropy loss in the conditional-discriminative network to
penalize the negative samples which hinder the convergence of the quality
regression network. Experimental results show that the proposed method can
achieve higher performance than traditional no-reference metrics, even
comparable results with full-reference metrics. The proposed method also
suggests the feasibility of assessing the quality of specific media content
without the expensive and cumbersome subjective evaluations. Code is available
at https://github.com/Qi-Yangsjtu/IT-PCQA.
