Summary of 3d Point Cloud Compression with Recurrent Neural Network and Image Compression Methods, by Till Beemelmanns et al.
3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods
by Till Beemelmanns, Yuchen Tao, Bastian Lampe, Lennart Reiher, Raphael van Kempen, Timo Woopen, Lutz Eckstein
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 3D-to-2D transformation allows for efficient compression of LiDAR point cloud data, essential for various autonomous vehicle applications. By transforming the raw data into a dense matrix structure, existing image compression methods and self-supervised deep compression approaches can be applied. The method outperforms generic octree-based and raw point cloud compression methods in terms of both quantitative and visual performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LiDAR point cloud data is important for autonomous vehicles. Right now, it’s hard to shrink this data without losing important information. One way to make this data smaller is to turn it into a 2D picture-like format. This makes it easier to use image compression tools. The researchers came up with a new way to do this transformation and used it to compress the data. They also figured out how to make the intensity measurements (which tell us how bright or dark something is) more compact. Their method works better than others in making the data smaller without losing its usefulness. |
Keywords
» Artificial intelligence » Self supervised
