Composing Error Concealment Pipelines for Dynamic 3D Point Cloud Streaming

• Published in: ACM transactions on multimedia computing communications and applications Vol. 21; no. 6; pp. 1 - 28
• Main Authors: Huang, I-Chun, Shi, Yuang, Sun, Yuan-Chun, Ooi, Wei Tsang, Huang, Chun-Ying, Hsu, Cheng-Hsin
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 01.07.2025
• Subjects: Computing methodologies; Information systems; Multimedia streaming; Point-based models; Reconstruction; error concealment; streaming; quality optimization; Dynamic 3D scenes
• ISSN: 1551-6857; 1551-6865
• DOI: 10.1145/3731561

Dynamic 3D point clouds enable the immersive user experience and thus have become increasingly more popular in volumetric video streaming applications. When being streamed over best-effort networks, point cloud frames may suffer from lost or late packets, leading to non-trivial quality degradation. To solve this problem, we proposed the very first error concealment pipeline framework, which comprises five stages: pre-processing, matching, motion estimation, prediction, and post-processing. Alternative algorithms can be developed for each stage, while algorithms of different stages could be mixed and matched into pipelines for end-to-end performance evaluations. We discussed the design goal and proposed multiple algorithms for each stage. These algorithms were then quantitatively compared using dynamic 3D point cloud sequences with diverse characteristics. Based on the comparison results, we proposed four representative pipelines for: (i) diverse degrees of motion variance, i.e., minor versus significant, and (ii) different application requirements, i.e., high quality versus low overhead. Extensive end-to-end evaluations of our proposed pipelines demonstrated their superior concealed quality over the 3D frame-copy method in both: (i) 3D metrics, by up to 5.32 dB in GPSNR and 1.7 dB in CPSNR,and (ii) 2D metrics, by up to 2.22 dB in PSNR, 0.06 in SSIM, and 11.67 in VMAF. Adding to that, a user study with 15 subjects indicated that our best-performing pipeline achieved 100% preference winning rate over the state-of-the-art learning-based interpolation algorithms while consuming merely up to 8.55% of running time.