  Photo-realistic point cloud capture and transmission are the fundamental
enablers for immersive visual communication. The coding process of dynamic
point clouds, especially video-based point cloud compression (V-PCC) developed
by the MPEG standardization group, is now delivering state-of-the-art
performance in compression efficiency. V-PCC is based on the projection of the
point cloud patches to 2D planes and encoding the sequence as 2D texture and
geometry patch sequences. However, the resulting quantization errors from
coding can introduce compression artifacts, which can be very unpleasant for
the quality of experience (QoE). In this work, we developed a novel
out-of-the-loop point cloud geometry artifact removal solution that can
significantly improve reconstruction quality without additional bandwidth cost.
Our novel framework consists of a point cloud sampling scheme, an artifact
removal network, and an aggregation scheme. The point cloud sampling scheme
employs a cube-based neighborhood patch extraction to divide the point cloud
into patches. The geometry artifact removal network then processes these
patches to obtain artifact-removed patches. The artifact-removed patches are
then merged together using an aggregation scheme to obtain the final
artifact-removed point cloud. We employ 3D deep convolutional feature learning
for geometry artifact removal that jointly recovers both the quantization
direction and the quantization noise level by exploiting projection and
quantization prior. The simulation results demonstrate that the proposed method
is highly effective and can considerably improve the quality of the
reconstructed point cloud.
