  Recent years have witnessed the success of the deep learning-based technique
in research of no-reference point cloud quality assessment (NR-PCQA). For a
more accurate quality prediction, many previous studies have attempted to
capture global and local feature in a bottom-up manner, but ignored the
interaction and promotion between them. To solve this problem, we propose a
novel asynchronous feedback network (AFNet). Motivated by human visual
perception mechanisms, AFNet employs a dual-branch structure to deal with
global and local feature, simulating the left and right hemispheres of the
human brain, and constructs a feedback module between them. Specifically, the
input point clouds are first fed into a transformer-based global encoder to
generate the attention maps that highlight these semantically rich regions,
followed by being merged into the global feature. Then, we utilize the
generated attention maps to perform dynamic convolution for different semantic
regions and obtain the local feature. Finally, a coarse-to-fine strategy is
adopted to merge the two features into the final quality score. We conduct
comprehensive experiments on three datasets and achieve superior performance
over the state-of-the-art approaches on all of these datasets. The code will be
available at https://github.com/zhangyujie-1998/AFNet.
