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: this https URL

基于深度学习的质量评估在感知多媒体质量评估方面取得了显著的提升，然而对于 3D 点云等 3D 视觉数据仍处于初级阶段。因此，我们提出了一种给定 3D 点云的无参考质量度量方法，通过集成频率幅度作为空间降解模式的指标来评估由压缩引起的质量影响，并使用轻量级混合深度模型进行质量评分映射。实验证明我们的方法在 PointXR 上优于最先进的非参考点云质量评估方法甚至一些全参考点云质量评估方法。