  LiDAR point cloud streams are usually sparse in time dimension, which is
limited by hardware performance. Generally, the frame rates of mechanical LiDAR
sensors are 10 to 20 Hz, which is much lower than other commonly used sensors
like cameras. To overcome the temporal limitations of LiDAR sensors, a novel
task named Point Cloud Frame Interpolation is studied in this paper. Given two
consecutive point cloud frames, Point Cloud Frame Interpolation aims to
generate intermediate frame(s) between them. To achieve that, we propose a
novel framework, namely Point Cloud Frame Interpolation Network (PointINet).
Based on the proposed method, the low frame rate point cloud streams can be
upsampled to higher frame rates. We start by estimating bi-directional 3D scene
flow between the two point clouds and then warp them to the given time step
based on the 3D scene flow. To fuse the two warped frames and generate
intermediate point cloud(s), we propose a novel learning-based points fusion
module, which simultaneously takes two warped point clouds into consideration.
We design both quantitative and qualitative experiments to evaluate the
performance of the point cloud frame interpolation method and extensive
experiments on two large scale outdoor LiDAR datasets demonstrate the
effectiveness of the proposed PointINet. Our code is available at
https://github.com/ispc-lab/PointINet.git.
