  Point Cloud Interpolation confronts challenges from point sparsity, complex
spatiotemporal dynamics, and the difficulty of deriving complete 3D point
clouds from sparse temporal information. This paper presents NeuroGauss4D-PCI,
which excels at modeling complex non-rigid deformations across varied dynamic
scenes. The method begins with an iterative Gaussian cloud soft clustering
module, offering structured temporal point cloud representations. The proposed
temporal radial basis function Gaussian residual utilizes Gaussian parameter
interpolation over time, enabling smooth parameter transitions and capturing
temporal residuals of Gaussian distributions. Additionally, a 4D Gaussian
deformation field tracks the evolution of these parameters, creating continuous
spatiotemporal deformation fields. A 4D neural field transforms low-dimensional
spatiotemporal coordinates ($x,y,z,t$) into a high-dimensional latent space.
Finally, we adaptively and efficiently fuse the latent features from neural
fields and the geometric features from Gaussian deformation fields.
NeuroGauss4D-PCI outperforms existing methods in point cloud frame
interpolation, delivering leading performance on both object-level (DHB) and
large-scale autonomous driving datasets (NL-Drive), with scalability to
auto-labeling and point cloud densification tasks. The source code is released
at https://github.com/jiangchaokang/NeuroGauss4D-PCI.
