Summary of No-reference Point Cloud Quality Assessment Via Graph Convolutional Network, by Wu Chen et al.
No-Reference Point Cloud Quality Assessment via Graph Convolutional Network
by Wu Chen, Qiuping Jiang, Wei Zhou, Feng Shao, Guangtao Zhai, Weisi Lin
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel, no-reference point cloud quality assessment (PCQA) method is proposed to characterize the mutual dependencies of multi-view 2D projected image contents using a graph convolutional network (GCN). The GCN-based PCQA (GC-PCQA) consists of three modules: multi-view projection, graph construction, and GCN-based quality prediction. This approach outperforms state-of-the-art quality assessment metrics on two publicly available benchmark databases. The code is available at https://github.com/chenwuwq/GC-PCQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to check how good point cloud pictures are without needing any reference images is developed. It uses a special kind of computer network called a graph convolutional network (GCN) to understand the relationships between many 2D images made from the same 3D picture. This method works by first turning the 3D point cloud into many 2D images, then building a special graph that shows how these images are related. The GCN then looks at this graph and uses it to predict how good or bad the original point cloud is. This new method does better than other methods for checking point cloud quality. |
Keywords
» Artificial intelligence » Convolutional network » Gcn
