Summary of Rethinking Data Input For Point Cloud Upsampling, by Tongxu Zhang
Rethinking Data Input for Point Cloud Upsampling
by Tongxu Zhang
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed research introduces a novel approach to point cloud upsampling by exploring differences in inputs between patch-based and full-input methods. The study utilizes a new data input method that divides the full point cloud model to ensure shape integrity while training PU-GCN, a Graph Convolutional Network (GCN) model. Experiments were conducted on the PU1K and ABC datasets, revealing that patch-based performance outperforms model-based full input. This paper investigates factors affecting upsampling results, shedding light on best practices for point cloud processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point clouds are used to reconstruct 3D scenes and generate surfaces. To improve these processes, scientists have developed methods to increase the density of point clouds. One way to do this is by dividing the data into smaller patches and then combining them. However, researchers haven’t compared this approach to using the entire point cloud as input. This study looks at a new method that breaks down the full point cloud into pieces while training a specific type of neural network called PU-GCN. The results show that patch-based methods perform better than using the whole point cloud. The goal is to understand what makes these differences happen. |
Keywords
* Artificial intelligence * Convolutional network * Gcn * Neural network
