LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression

Описание: The rapid advancements in artificial intelligence (AI) and machine learning (ML) have led to significant improvements in various domains, including natural language processing (NLP) and computer vision. One of the emerging areas of interest is the application of Large Language Models (LLMs) in point cloud geometry compression (PCGC). This report delves into the novel architecture known as Large Language Model-based Point Cloud Geometry Compression (LLM-PCGC), which leverages LLMs to compress point cloud geometry information without relying on text descriptions or aligning operations. The report will explore the methodologies, techniques, and experimental results associated with LLM-PCGC, providing a comprehensive understanding of its capabilities and performance.