Simple Baselines for Projection-based Full-reference and No-reference
  Point Cloud Quality Assessment
Zicheng Zhang Yingjie Zhou Wei Sun Xiongkuo Min Guangtao Zhai
Point clouds are widely used in 3D content representation and have various
applications in multimedia. However, compression and simplification processes
inevitably result in the loss of quality-aware information under storage and
bandwidth constraints. Therefore, there is an increasing need for effective
methods to quantify the degree of distortion in point clouds. In this paper, we
propose simple baselines for projection-based point cloud quality assessment
(PCQA) to tackle this challenge. We use multi-projections obtained via a common
cube-like projection process from the point clouds for both full-reference (FR)
and no-reference (NR) PCQA tasks. Quality-aware features are extracted with
popular vision backbones. The FR quality representation is computed as the
similarity between the feature maps of reference and distorted projections
while the NR quality representation is obtained by simply squeezing the feature
maps of distorted projections with average pooling The corresponding quality
representations are regressed into visual quality scores by fully-connected
layers. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving
the top spot in four out of the five competition tracks.