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arXiv Computer Vision By Xinghui Tao, Zehao Ye, Guangming Wang, Jelena Nini\'c, Brian Sheil

R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds

Read the original on arXiv Computer Vision →

R4Tun is a large language model–driven framework that adapts an expert-designed segmentation pipeline (SAM4Tun) for segmental tunnel lining inspection from 3D point clouds. By tuning bounded parameters guided by structured context—memory, state, and knowledge—it improves mean Intersection-over-Union from 0.18 to 0.43–0.48 and overall accuracy from 0.42 to 0.59–0.65 across 30 tunnel subsets and three LLMs, with the best regular subsets achieving mIoU up to 0.796. The method consistently adjusts the same critical parameters across runs, offering a controlled, label‑free, cross‑LLM adaptation mechanism with auditable rationales.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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