  In recent years, there has been a significant increase in focus on the
interpolation task of computer vision. Despite the tremendous advancement of
video interpolation, point cloud interpolation remains insufficiently explored.
Meanwhile, the existence of numerous nonlinear large motions in real-world
scenarios makes the point cloud interpolation task more challenging. In light
of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural
field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame
information to handle nonlinear large motions for both indoor and outdoor
scenarios. Furthermore, we construct a new multi-frame point cloud
interpolation dataset called NL-Drive for large nonlinear motions in autonomous
driving scenes to better demonstrate the superiority of our method. Ultimately,
NeuralPCI achieves state-of-the-art performance on both DHB (Dynamic Human
Bodies) and NL-Drive datasets. Beyond the interpolation task, our method can be
naturally extended to point cloud extrapolation, morphing, and auto-labeling,
which indicates its substantial potential in other domains. Codes are available
at https://github.com/ispc-lab/NeuralPCI.
