  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 weighted 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. Objective comparison results show that
our proposed method achieves an average of 9.74% BD-rate reduction compared
with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective
comparison results present that visual artifacts such as color shifting,
blurring, and quantization noise are reduced.
