Publications

@article{xie_llm-guided_2024,

title = {LLM-Guided Cross-Modal Point Cloud Quality Assessment: A Graph Learning Approach},

author = {W. Xie and Y. Liu and K. Wang and M. Wang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203417746&doi=10.1109%2fLSP.2024.3452556&partnerID=40&md5=88460ec3043fa9161c4d5dd6fc282f95},

doi = {10.1109/LSP.2024.3452556},

issn = {10709908 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Signal Processing Letters},

volume = {31},

pages = {2250–2254},

abstract = {This paper addresses the critical need for accurate and reliable point cloud quality assessment (PCQA) in various applications, such as autonomous driving, robotics, virtual reality, and 3D reconstruction. To meet this need, we propose a large language model (LLM)-guided PCQA approach based on graph learning. Specifically, we first utilize the LLM to generate quality description texts for each 3D object, and employ two CLIP-like feature encoders to represent the image and text modalities. Next, we design a latent feature enhancer module to improve contrastive learning, enabling more effective alignment performance. Finally, we develop a graph network fusion module that utilizes a ranking-based loss to adjust the relationship of different nodes, which explicitly considers both modality fusion and quality ranking. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach over 12 representative PCQA methods, which demonstrate the potential of multi-modal learning, the importance of latent feature enhancement, and the significance of graph-based fusion in advancing the field of PCQA. © 2024 IEEE.},

keywords = {3D reconstruction, Cross-modal, Language Model, Large language model, Learning approach, Multi-modal, Multimodal quality assessment, Point cloud quality assessment, Point-clouds, Quality assessment},

pubstate = {published},

tppubtype = {article}

}