  Pseudo-LiDAR point cloud interpolation is a novel and challenging task in the
field of autonomous driving, which aims to address the frequency mismatching
problem between camera and LiDAR. Previous works represent the 3D spatial
motion relationship induced by a coarse 2D optical flow, and the quality of
interpolated point clouds only depends on the supervision of depth maps. As a
result, the generated point clouds suffer from inferior global distributions
and local appearances. To solve the above problems, we propose a Pseudo-LiDAR
point cloud interpolation network to generates temporally and spatially
high-quality point cloud sequences. By exploiting the scene flow between point
clouds, the proposed network is able to learn a more accurate representation of
the 3D spatial motion relationship. For the more comprehensive perception of
the distribution of point cloud, we design a novel reconstruction loss function
that implements the chamfer distance to supervise the generation of
Pseudo-LiDAR point clouds in 3D space. In addition, we introduce a multi-modal
deep aggregation module to facilitate the efficient fusion of texture and depth
features. As the benefits of the improved motion representation, training loss
function, and model structure, our approach gains significant improvements on
the Pseudo-LiDAR point cloud interpolation task. The experimental results
evaluated on KITTI dataset demonstrate the state-of-the-art performance of the
proposed network, quantitatively and qualitatively.
