No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction

Abstract

With the rapid development of 3D vision applications based on point clouds, point cloud quality assessment(PCQA) is becoming an important research topic. However, the prior PCQA methods ignore the effect of local quality variance across different areas of the point cloud. To take an advantage of the quality distribution imbalance, we propose a no-reference point cloud quality assessment (NR-PCQA) method with local area correlation analysis capability, denoted as COPP-Net. More specifically, we split a point cloud into patches, generate texture and structure features for each patch, and fuse them into patch features to predict patch quality. Then, we gather the features of all the patches of a point cloud for correlation analysis, to obtain the correlation weights. Finally, the predicted qualities and correlation weights for all the patches are used to derive the final quality score. Experimental results show that our method outperforms the state-of-the-art benchmark NR-PCQA methods. The source code for the proposed COPP-Net can be found at https://github.com/philox12358/COPP-Net.

arXiv comment6 pages, 5 figures, Accepted by International Conference on Software Engineering and Knowledge Engineering(SEKE2023)

References in corpus (10)

• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
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• No-Reference Point Cloud Quality Assessment via Domain Adaptation
• k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation

Cited by in corpus (1)

• Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features