Summary of Att2cpc: Attention-guided Lossy Attribute Compression Of Point Clouds, by Kai Liu et al.
Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds
by Kai Liu, Kang You, Pan Gao, Manoranjan Paul
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 A novel attention-based method is proposed for learned lossy point cloud attribute compression (PCAC) leveraging an autoencoder architecture. The approach efficiently compresses point cloud attributes by exploiting local patterns, integrating geometry and attribute contexts using External Cross Attention (ECA). This hierarchical aggregation enables progressive reconstruction of attributes at the decoding side. The method achieves state-of-the-art results on various sequences, including human body frames, sparse objects, and large-scale scenes, with average improvements of 1.15 dB and 2.13 dB in BD-PSNR for Y channel and YUV channel, respectively, compared to Deep-PCAC. This is the first approach introducing attention mechanisms to the point-based lossy PCAC task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point clouds are like super detailed pictures of the world around us. With all this new technology making these kinds of pictures, we need ways to make them smaller and easier to store without losing important details. This paper talks about a way to do just that – compressing point cloud attributes using an attention-based method. It’s kind of like how our brains focus on certain things while ignoring others. The method is really good at keeping the important parts of the picture and discarding the less important ones. It even beats other ways of doing this task, making it a big deal in the world of computer vision. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Cross attention
