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Point clouds are widely used in 3D content representation and have variousapplications in multimedia. However, compression and simplification processesinevitably result in the loss of quality-aware information under storage andbandwidth constraints. Therefore, there is an increasing need for effectivemethods to quantify the degree of distortion in point clouds. In this paper, wepropose simple baselines for projection-based point cloud quality assessment(PCQA) to tackle this challenge. We use multi-projections obtained via a commoncube-like projection process from the point clouds for both full-reference (FR)and no-reference (NR) PCQA tasks. Quality-aware features are extracted withpopular vision backbones. The FR quality representation is computed as thesimilarity between the feature maps of reference and distorted projectionswhile the NR quality representation is obtained by simply squeezing the featuremaps of distorted projections with average pooling The corresponding qualityrepresentations are regressed into visual quality scores by fully-connectedlayers. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achievingthe top spot in four out of the five competition tracks.

Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment