Summary of Llm-pcgc: Large Language Model-based Point Cloud Geometry Compression, by Yuqi Ye et al.
LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression
by Yuqi Ye, Wei Gao
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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 The paper explores the use of large language models (LLMs) for lossless point cloud geometry compression (PCGC). LLMs have shown promise in both generating contextual data and compressing it, making them a suitable choice for PCGC tasks. However, applying LLMs directly to PCGC tasks poses challenges due to the lack of understanding of point cloud structures and the difficulty in bridging the gap between text descriptions and point clouds. To address these issues, the authors introduce the Large Language Model-based Point Cloud Geometry Compression (LLM-PCGC) method, which uses LLMs to compress point cloud geometry information without requiring text descriptions or alignment operations. The proposed method employs various adaptation techniques for cross-modality representation alignment and semantic consistency, including clustering, K-tree, token mapping invariance, and Low Rank Adaptation (LoRA). Experimental results demonstrate that the LLM-PCGC outperforms existing methods by achieving -40.213% bit rate reduction compared to the reference software of MPEG Geometry-based Point Cloud Compression (G-PCC) standard, and by achieving -2.267% bit rate reduction compared to the state-of-the-art learning-based method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses large language models (LLMs) to compress point cloud geometry information without needing text descriptions or alignment operations. This is important because it helps us understand how to make 3D data, like point clouds, smaller and more efficient. The authors introduce a new way of doing this called LLM-PCGC, which is better than other methods at reducing the size of the data. |
Keywords
» Artificial intelligence » Alignment » Clustering » Large language model » Lora » Low rank adaptation » Token
