NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
Chaokang Jiang, Dalong Du, Jiuming Liu, Siting Zhu, Zhenqiang Liu,, Zhuang Ma, Zhujin Liang, Jie Zhou

TL;DR
NeuroGauss4D-PCI introduces a novel 4D neural and Gaussian deformation field approach for accurate point cloud interpolation, effectively modeling complex non-rigid deformations in dynamic scenes.
Contribution
The paper proposes a new method combining Gaussian residuals and 4D neural fields for improved point cloud interpolation and deformation modeling.
Findings
Outperforms existing methods on object-level and autonomous driving datasets.
Effectively captures complex non-rigid deformations.
Scalable to auto-labeling and densification tasks.
Abstract
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 () into a…
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Taxonomy
TopicsOptical measurement and interference techniques · 3D Shape Modeling and Analysis · Advanced Measurement and Metrology Techniques
