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.

我们提出了基于投影的点云质量评估的简单基准线方法，通过使用点云的多投影来提取质量感知特征，将完整参考质量表示和非参考质量表示回归到视觉质量分数，以应对点云在存储和带宽限制下失真的需求。我们在 ICIP 2023 PCVQA Challenge 中取得了四个赛道的冠军。