Kavli Affiliate: Wei Gao
| First 5 Authors: Songlin Fan, Wei Gao, Zhineng Chen, Ge Li, Guoqing Liu
| Summary:
The evolution of point cloud processing algorithms necessitates an accurate
assessment for their quality. 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. 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. Codes are available at
https://git.openi.org.cn/OpenPointCloud/nrpcqa.
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