Attribute Artifacts Removal for Geometry-based Point Cloud Compression

• URL: http://arxiv.org/abs/2112.00560v1
• Date: Wed, 1 Dec 2021 15:21:06 GMT
• Title: Attribute Artifacts Removal for Geometry-based Point Cloud Compression
• Authors: Xihua Sheng, Li Li, Dong Liu, Zhiwei Xiong
• Abstract summary: Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. It still leads to serious attribute compression artifacts, especially under low scenarios. We propose a Multi-Scale Graph Attention Network (MSGAT) to remove the artifacts of point cloud attributes.
• Score: 43.60640890971367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, especially under low bitrate scenarios. In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC. We first construct a graph based on point cloud geometry coordinates and then use the Chebyshev graph convolutions to extract features of point cloud attributes. Considering that one point may be correlated with points both near and far away from it, we propose a multi-scale scheme to capture the short and long range correlations between the current point and its neighboring and distant points. To address the problem that various points may have different degrees of artifacts caused by adaptive quantization, we introduce the quantization step per point as an extra input to the proposed network. We also incorporate a graph attentional layer into the network to pay special attention to the points with more attribute artifacts. To the best of our knowledge, this is the first attribute artifacts removal method for G-PCC. We validate the effectiveness of our method over various point clouds. Experimental results show that our proposed method achieves an average of 9.28% BD-rate reduction. In addition, our approach achieves some performance improvements for the downstream point cloud semantic segmentation task.

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