  Deep learning-based quality assessments have significantly enhanced
perceptual multimedia quality assessment, however it is still in the early
stages for 3D visual data such as 3D point clouds (PCs). Due to the high volume
of 3D-PCs, such quantities are frequently compressed for transmission and
viewing, which may affect perceived quality. Therefore, we propose no-reference
quality metric of a given 3D-PC. Comparing to existing methods that mostly
focus on geometry or color aspects, we propose integrating frequency magnitudes
as indicator of spatial degradation patterns caused by the compression. To map
the input attributes to quality score, we use a light-weight hybrid deep model;
combined of Deformable Convolutional Network (DCN) and Vision Transformers
(ViT). Experiments are carried out on ICIP20 [1], PointXR [2] dataset, and a
new big dataset called BASICS [3]. The results show that our approach
outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR.
The implementation code can be found at: https://github.com/o-messai/3D-PCQA
