Summary of Implicit Neural Compression Of Point Clouds, by Hongning Ruan et al.
Implicit Neural Compression of Point Clouds
by Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao, Zhaoyang Zhang, Dusit Niyato
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 This paper proposes NeRC3, a novel framework for compressing unstructured point cloud data using implicit neural representations. The approach employs two coordinate-based neural networks to represent the geometry and attributes of voxelized point clouds. The neural network parameters are quantized and compressed alongside auxiliary information required for reconstruction. The authors also extend their method to dynamic point cloud compression, reducing temporal redundancy with techniques like 4D spatial-temporal representation. Experimental results show that NeRC3 outperforms octree-based methods in the latest G-PCC standard for static point clouds, while 4D-NeRC3 demonstrates superior geometry compression and competitive results for joint geometry and attribute compression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point cloud compression is a big challenge because it’s hard to shrink down lots of precise information without losing important details. This paper shows how to do it using special neural networks that can learn about point clouds and compress them in a way that preserves the most important info. They also show how to make this work for moving point clouds, which has its own set of challenges. |
Keywords
» Artificial intelligence » Neural network
