Uncertainty-aware No-Reference Point Cloud Quality Assessment
Songlin Fan Zixuan Guo Wei Gao Ge Li
The evolution of compression and enhancement algorithms necessitates an
accurate quality assessment for point clouds. Previous works consistently
regard point cloud quality assessment (PCQA) as a MOS regression problem and
devise a deterministic mapping, ignoring the stochasticity in generating MOS
from subjective tests. Besides, the viewpoint switching of 3D point clouds in
subjective tests reinforces the judging stochasticity of different subjects
compared with traditional images. This work presents the first probabilistic
architecture for no-reference PCQA, motivated by the labeling process of
existing datasets. The proposed method can model the quality judging
stochasticity of subjects through a tailored conditional variational
autoencoder (CVAE) and produces multiple intermediate quality ratings. These
intermediate ratings simulate the judgments from different subjects and are
then integrated into an accurate quality prediction, mimicking the generation
process of a ground truth MOS. Specifically, our method incorporates a Prior
Module, a Posterior Module, and a Quality Rating Generator, where the former
two modules are introduced to model the judging stochasticity in subjective
tests, while the latter is developed to generate diverse quality ratings.
Extensive experiments indicate that our approach outperforms previous
cutting-edge methods by a large margin and exhibits gratifying cross-dataset
robustness.