  LiDAR point cloud frame interpolation, which synthesizes the intermediate
frame between the captured frames, has emerged as an important issue for many
applications. Especially for reducing the amounts of point cloud transmission,
it is by predicting the intermediate frame based on the reference frames to
upsample data to high frame rate ones. However, due to high-dimensional and
sparse characteristics of point clouds, it is more difficult to predict the
intermediate frame for LiDAR point clouds than videos. In this paper, we
propose a novel LiDAR point cloud frame interpolation method, which exploits
range images (RIs) as an intermediate representation with CNNs to conduct the
frame interpolation process. Considering the inherited characteristics of RIs
differ from that of color images, we introduce spatially adaptive convolutions
to extract range features adaptively, while a high-efficient flow estimation
method is presented to generate optical flows. The proposed model then warps
the input frames and range features, based on the optical flows to synthesize
the interpolated frame. Extensive experiments on the KITTI dataset have clearly
demonstrated that our method consistently achieves superior frame interpolation
results with better perceptual quality to that of using state-of-the-art video
frame interpolation methods. The proposed method could be integrated into any
LiDAR point cloud compression systems for inter prediction.
