2021/09/15 by Yipeng Liu, Liu, Yipeng, Qi Yang +5
Computer Science · Engineering · Environmental Science · #3D Shape Modeling and Analysis #Computer Graphics and Visualization Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote Sensing and LiDAR Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.07158
openalex publication_date 2021/09/15 · openalex created_date 2021/09/27 · openalex updated_date 2026/07/28
Point cloud compression (PCC) has made remarkable achievement in recent years. In the mean time, point cloud quality assessment (PCQA) also realize gratifying development. Some recently emerged metrics present robust performance on public point cloud assessment databases. However, these metrics have not been evaluated specifically for PCC to verify whether they exhibit consistent performance with the subjective perception. In this paper, we establish a new dataset for compression evaluation first, which contains 175 compressed point clouds in total, deriving from 7 compression algorithms with 5 compression levels. Then leveraging the proposed dataset, we evaluate the performance of the existing PCQA metrics in terms of different compression types. The results demonstrate some deficiencies of existing metrics in compression evaluation.