Summary of Point Cloud Geometry Scalable Coding with a Quality-conditioned Latents Probability Estimator, by Daniele Mari et al.
Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator
by Daniele Mari, André F. R. Guarda, Nuno M. M. Rodrigues, Simone Milani, Fernando Pereira
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Scalable Quality Hyperprior (SQH) scheme addresses the quality scalability issue in learning-based point cloud geometry codecs. It uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode high-quality point clouds from lower-quality base layers, enabling progressive decoding with increasing quality and fidelity. SQH is integrated into the JPEG PC coding standard, allowing for layered bitstreams that preserve compression gains while offering quality scalability. The scheme achieves this feat without compromising compression performance, making it a valuable addition to existing point cloud codecs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SQH is a new way to decode point clouds so they look better on different devices and networks. Right now, when you try to show 3D models on different devices, the quality can be really bad or really good depending on how strong your device is. SQH helps fix this by letting you start with a lower-quality version of the model and then make it better as needed. This means that even if your device isn’t very powerful, you’ll still get to see a pretty good 3D model. |
Keywords
» Artificial intelligence » Probability
