Summary of Neurogauss4d-pci: 4d Neural Fields and Gaussian Deformation Fields For Point Cloud Interpolation, by Chaokang Jiang et al.
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
by Chaokang Jiang, Dalong Du, Jiuming Liu, Siting Zhu, Zhenqiang Liu, Zhuang Ma, Zhujin Liang, Jie Zhou
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents NeuroGauss4D-PCI, a novel method for Point Cloud Interpolation that excels at modeling complex non-rigid deformations across varied dynamic scenes. The approach begins with an iterative Gaussian cloud soft clustering module to create structured temporal point cloud representations. A 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. The method also involves adapting neural fields to transform low-dimensional spatiotemporal coordinates into high-dimensional latent space and fusing latent features with geometric features from Gaussian deformation fields. NeuroGauss4D-PCI outperforms existing methods in point cloud frame interpolation on object-level (DHB) and large-scale autonomous driving datasets (NL-Drive), demonstrating scalability to auto-labeling and point cloud densification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to fill gaps in 3D images taken from different angles. The method, called NeuroGauss4D-PCI, is good at handling complex movements across various scenes. It works by first grouping points into clusters based on their similarity. Then, it uses a special kind of function to smoothly connect the clusters over time. This allows the method to capture the subtle changes in the scene as objects move or deform. The result is a more accurate and detailed 3D image that can be used for tasks like autonomous driving or object recognition. |
Keywords
» Artificial intelligence » Clustering » Latent space » Spatiotemporal
