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 this https URL

该论文介绍了一种名为 NeuroGauss4D-PCI 的方法，通过模拟非刚性变形和对参数进行插值来解决点云插值问题，并在点云帧插值中表现出优越性能。

NeuroGauss4D-PCI: 基于 4D 神经场和高斯变形场的点云插值

NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for  Point Cloud Interpolation

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 this https URL

该研究提出了 NeuralPCI，这是一个能隐式整合多帧信息用于处理室内和室外大非线性运动的 4D 时空神经场，以及一个名为 NL-Drive 的新的点云插值数据集。该方法在 DHB 和 NL-Drive 数据集上取得了最先进的性能，并在点云外推、变形和自动标记等其他领域具有潜在的应用价值。