  The non-uniformly distributed nature of the 3D dynamic point cloud (DPC)
brings significant challenges to its high-efficient inter-frame compression.
This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point
Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry
with 3D motion estimation and motion compensation in the feature space. In the
proposed D-DPCC network, we design a {\it Multi-scale Motion Fusion} (MMF)
module to accurately estimate the 3D optical flow between the feature
representations of adjacent point cloud frames. Specifically, we utilize a 3D
sparse convolution-based encoder to obtain the latent representation for motion
estimation in the feature space and introduce the proposed MMF module for fused
3D motion embedding. Besides, for motion compensation, we propose a 3D {\it
Adaptively Weighted Interpolation} (3DAWI) algorithm with a penalty coefficient
to adaptively decrease the impact of distant neighbors. We compress the motion
embedding and the residual with a lossy autoencoder-based network. To our
knowledge, this paper is the first work proposing an end-to-end deep dynamic
point cloud compression framework. The experimental result shows that the
proposed D-DPCC framework achieves an average 76\% BD-Rate (Bjontegaard Delta
Rate) gains against state-of-the-art Video-based Point Cloud Compression
(V-PCC) v13 in inter mode.
