Kavli Affiliate: Wei Gao
| First 5 Authors: Yuqi Ye, Wei Gao, , ,
| Summary:
The key to effective point cloud compression is to obtain a robust context
model consistent with complex 3D data structures. Recently, the advancement of
large language models (LLMs) has highlighted their capabilities not only as
powerful generators for in-context learning and generation but also as
effective compressors. These dual attributes of LLMs make them particularly
well-suited to meet the demands of data compression. Therefore, this paper
explores the potential of using LLM for compression tasks, focusing on lossless
point cloud geometry compression (PCGC) experiments. However, applying LLM
directly to PCGC tasks presents some significant challenges, i.e., LLM does not
understand the structure of the point cloud well, and it is a difficult task to
fill the gap between text and point cloud through text description, especially
for large complicated and small shapeless point clouds. To address these
problems, we introduce a novel architecture, namely the Large Language
Model-based Point Cloud Geometry Compression (LLM-PCGC) method, using LLM to
compress point cloud geometry information without any text description or
aligning operation. By utilizing different adaptation techniques for
cross-modality representation alignment and semantic consistency, including
clustering, K-tree, token mapping invariance, and Low Rank Adaptation (LoRA),
the proposed method can translate LLM to a compressor/generator for point
cloud. To the best of our knowledge, this is the first structure to employ LLM
as a compressor for point cloud data. Experiments demonstrate that the LLM-PCGC
outperforms the other existing methods significantly, 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.
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