  3D dynamic point cloud (DPC) compression relies on mining its temporal
context, which faces significant challenges due to DPC's sparsity and
non-uniform structure. Existing methods are limited in capturing sufficient
temporal dependencies. Therefore, this paper proposes a learning-based DPC
compression framework via hierarchical block-matching-based inter-prediction
module to compensate and compress the DPC geometry in latent space.
Specifically, we propose a hierarchical motion estimation and motion
compensation (Hie-ME/MC) framework for flexible inter-prediction, which
dynamically selects the granularity of optical flow to encapsulate the motion
information accurately. To improve the motion estimation efficiency of the
proposed inter-prediction module, we further design a KNN-attention block
matching (KABM) network that determines the impact of potential corresponding
points based on the geometry and feature correlation. Finally, we compress the
residual and the multi-scale optical flow with a fully-factorized deep entropy
model. The experiment result on the MPEG-specified Owlii Dynamic Human Dynamic
Point Cloud (Owlii) dataset shows that our framework outperforms the previous
state-of-the-art methods and the MPEG standard V-PCC v18 in inter-frame
low-delay mode.
