Chao YIN (尹超)

Research Scientist

Point Cloud Intelligent Processing & BIM Modeling

I am a research scientist working on point cloud intelligent processing and BIM modeling. I earned my PhD from the Hong Kong University of Science and Technology (HKUST) from 2018 Aug to 2022 Aug, advised by Prof. Jack C.P. Cheng, and my MPhil in Cartography and Geographic Information Systems from Wuhan University (2013), advised by Prof. Zhongliang Fu. My research spans 3D point cloud processing, deep learning, multi-modal AI (foundation models and 2D/3D LLMs), weakly-supervised and long-tailed learning, and remote sensing applications.

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From Geometric Labels to Semantic Understanding of Indoor Building Components Using Multimodal Large Language Models

S. Jing, Chao YIN*

Automation in Construction, 2026 JCR Q1, IF: 10.5 [Accepted]

BibTeX

@article{jing2026geometric,
  title   = {From Geometric Labels to Semantic Understanding of Indoor Building Components Using Multimodal Large Language Models},
  author  = {Jing, S. and Yin, Chao},
  journal = {Automation in Construction},
  year    = {2026}
}

A multimodal large language model framework that moves beyond geometric labels toward semantic understanding of indoor building components.

Semi-Supervised AI for Architectural Heritage Classification and Style Lineage Discovery in Chinese Traditional Settlements

Q. Han, Z. Wang, Chao YIN*, Z. Hou, T. Yao

ISPRS International Journal of Geo-Information, vol. 15, no. 5, p. 221, 2026 MDPI

DOI BibTeX

@article{han2026semisupervised,
  title   = {Semi-Supervised AI for Architectural Heritage Classification and Style Lineage Discovery in Chinese Traditional Settlements},
  author  = {Han, Q. and Wang, Z. and Yin, Chao and Hou, Z. and Yao, T.},
  journal = {ISPRS International Journal of Geo-Information},
  volume  = {15},
  number  = {5},
  pages   = {221},
  year    = {2026},
  doi     = {10.3390/ijgi15050221}
}

A semi-supervised AI framework for architectural heritage classification and style lineage discovery in Chinese traditional settlements.

Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes

B. Wang, Z. Chen, M. Li, Q. Wang, Chao YIN, J. C. P. Cheng

Automation in Construction, vol. 162, p. 105384, 2024 JCR Q1, IF: 10.5

DOI BibTeX

@article{wang2024omni,
  title   = {Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes},
  author  = {Wang, B. and Chen, Z. and Li, M. and Wang, Q. and Yin, Chao and Cheng, Jack C. P.},
  journal = {Automation in Construction},
  volume  = {162},
  pages   = {105384},
  year    = {2024},
  doi     = {10.1016/j.autcon.2024.105384}
}

A comprehensive Scan2BIM approach leveraging vision foundation models for automated MEP scene understanding and BIM reconstruction.

Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning

Chao YIN, B. Yang, J. C. P. Cheng*, V. J. L. Gan*, B. Wang, J. Yang

Automation in Construction, vol. 148, p. 104757, 2023 JCR Q1, IF: 10.5

DOI SQN-TensorFlow★ 14 SQN-PyTorch BibTeX

@article{yin2023label,
  title   = {Label-efficient semantic segmentation of large-scale industrial point clouds using weakly supervised learning},
  author  = {Yin, Chao and Yang, B. and Cheng, Jack C. P. and Gan, Vincent J. L. and Wang, B. and Yang, J.},
  journal = {Automation in Construction},
  volume  = {148},
  pages   = {104757},
  year    = {2023},
  doi     = {10.1016/j.autcon.2023.104757}
}

A weakly supervised learning framework for semantic segmentation of large-scale industrial point clouds that drastically reduces annotation effort while retaining accuracy.

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

D. Hu, V. J. L. Gan*, Chao YIN

Automation in Construction, vol. 152, p. 104949, 2023 JCR Q1, IF: 10.5

DOI BibTeX

@article{hu2023robot,
  title   = {Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors},
  author  = {Hu, D. and Gan, Vincent J. L. and Yin, Chao},
  journal = {Automation in Construction},
  volume  = {152},
  pages   = {104949},
  year    = {2023},
  doi     = {10.1016/j.autcon.2023.104949}
}

A robotic mobile scanning system integrating 3D reconstruction with real-time point cloud semantic segmentation for building interiors.

Automated Classification of Piping Components from 3D LiDAR Point Clouds using SE-PseudoGrid

Chao YIN, J. C. P. Cheng*, B. Wang, V. J. L. Gan*

Automation in Construction, vol. 139, p. 104300, 2022 JCR Q1, IF: 10.5

DOI Code BibTeX

@article{yin2022automated,
  title   = {Automated classification of piping components from 3D LiDAR point clouds using SE-PseudoGrid},
  author  = {Yin, Chao and Cheng, Jack C. P. and Wang, B. and Gan, Vincent J. L.},
  journal = {Automation in Construction},
  volume  = {139},
  pages   = {104300},
  year    = {2022},
  doi     = {10.1016/j.autcon.2022.104300}
}

SE-PseudoGrid, a squeeze-and-excitation network with pseudo-grid representation for automated piping-component classification from LiDAR data.

Towards Classification of Architectural Styles of Chinese Traditional Settlements Using Deep Learning: A Dataset, a New Framework, and Its Interpretability

Q. Han, Chao YIN*, Y. Deng, P. Liu

Remote Sensing, vol. 14, no. 20, 2022 JCR Q2, IF: 5.3

DOI BibTeX

@article{han2022towards,
  title   = {Towards Classification of Architectural Styles of Chinese Traditional Settlements Using Deep Learning: A Dataset, a New Framework, and Its Interpretability},
  author  = {Han, Q. and Yin, Chao and Deng, Y. and Liu, P.},
  journal = {Remote Sensing},
  volume  = {14},
  number  = {20},
  pages   = {5000},
  year    = {2022},
  doi     = {10.3390/rs14205000}
}

A new dataset and deep learning framework for architectural-style classification of Chinese traditional settlements, with interpretability analysis.

Vision-assisted BIM reconstruction from 3D LiDAR point clouds for MEP scenes

B. Wang, Q. Wang, J. C. P. Cheng, C. Song, Chao YIN

Automation in Construction, vol. 133, p. 103997, 2022 JCR Q1, IF: 10.5

DOI BibTeX

@article{wang2022vision,
  title   = {Vision-assisted BIM reconstruction from 3D LiDAR point clouds for MEP scenes},
  author  = {Wang, B. and Wang, Q. and Cheng, Jack C. P. and Song, C. and Yin, Chao},
  journal = {Automation in Construction},
  volume  = {133},
  pages   = {103997},
  year    = {2022},
  doi     = {10.1016/j.autcon.2021.103997}
}

A vision-assisted pipeline that reconstructs parametric BIM from 3D LiDAR point clouds of MEP scenes.

Object verification based on deep learning point feature comparison for scan-to-BIM

B. Wang, Q. Wang*, J. C. P. Cheng*, Chao YIN

Automation in Construction, vol. 142, p. 104515, 2022 JCR Q1, IF: 10.5

DOI BibTeX

@article{wang2022object,
  title   = {Object verification based on deep learning point feature comparison for scan-to-BIM},
  author  = {Wang, B. and Wang, Q. and Cheng, Jack C. P. and Yin, Chao},
  journal = {Automation in Construction},
  volume  = {142},
  pages   = {104515},
  year    = {2022},
  doi     = {10.1016/j.autcon.2022.104515}
}

A deep-learning point-feature comparison method that verifies reconstructed objects against scans for reliable scan-to-BIM.

Automated semantic segmentation of industrial point clouds using ResPointNet++

Chao YIN, B. Wang, V. J. L. Gan, M. Wang, J. C. P. Cheng*

Automation in Construction, vol. 130, p. 103874, 2021 JCR Q1, IF: 10.5

DOI Code★ 44 BibTeX

@article{yin2021automated,
  title   = {Automated semantic segmentation of industrial point clouds using ResPointNet++},
  author  = {Yin, Chao and Wang, B. and Gan, Vincent J. L. and Wang, M. and Cheng, Jack C. P.},
  journal = {Automation in Construction},
  volume  = {130},
  pages   = {103874},
  year    = {2021},
  doi     = {10.1016/j.autcon.2021.103874}
}

ResPointNet++, a deep residual point network for automated semantic segmentation of industrial MEP point clouds.

Fully automated generation of parametric BIM for MEP scenes based on terrestrial laser scanning data

B. Wang, Chao YIN, H. Luo, J. C. P. Cheng, Q. Wang

Automation in Construction, vol. 125, p. 103615, 2021 JCR Q1, IF: 10.5

DOI BibTeX

@article{wang2021fully,
  title   = {Fully automated generation of parametric BIM for MEP scenes based on terrestrial laser scanning data},
  author  = {Wang, B. and Yin, Chao and Luo, H. and Cheng, Jack C. P. and Wang, Q.},
  journal = {Automation in Construction},
  volume  = {125},
  pages   = {103615},
  year    = {2021},
  doi     = {10.1016/j.autcon.2021.103615}
}

A fully automated pipeline that generates parametric BIM for MEP scenes directly from terrestrial laser scanning data.

Industrial3D: A Terrestrial LiDAR Point Cloud Dataset and Cross-Paradigm Benchmark for Industrial Infrastructure

Chao YIN, H. Yue, Q. Han, D. Hu, Z. Liang, F. Lin, B. Sun, B. Wang, M. Li, W. Yao, J. C. P. Cheng

arXiv:2603.28660, 2026

arXiv Code & Dataset Project Page BibTeX

@article{yin2026industrial3d,
  title   = {Industrial3D: A Terrestrial LiDAR Point Cloud Dataset and Cross-Paradigm Benchmark for Industrial Infrastructure},
  author  = {Yin, Chao and Yue, H. and Han, Q. and Hu, D. and Liang, Z. and Lin, F. and Sun, B. and Wang, B. and Li, M. and Yao, W. and Cheng, Jack C. P.},
  journal = {arXiv preprint arXiv:2603.28660},
  year    = {2026}
}

Industrial3D — the largest terrestrial LiDAR dataset for industrial MEP facilities, with 612 million labelled points across 13 water treatment plants (6.6× larger than any comparable dataset). Its cross-paradigm benchmark evaluates 9 methods across supervised, weakly-supervised, unsupervised, and foundation-model settings.

Resolving Primitive-Sharing Ambiguity in Long-Tailed Industrial Point Cloud Segmentation via Spatial Context Constraints

Chao YIN, Q. Han, Z. Hou, Y. Liu, A. Dai, H. Hu, J. Yang, W. Yao

arXiv:2601.19128, 2026

arXiv Code BibTeX

@article{yin2026resolving,
  title   = {Resolving Primitive-Sharing Ambiguity in Long-Tailed Industrial Point Cloud Segmentation via Spatial Context Constraints},
  author  = {Yin, Chao and Han, Q. and Hou, Z. and Liu, Y. and Dai, A. and Hu, H. and Yang, J. and Yao, W.},
  journal = {arXiv preprint arXiv:2601.19128},
  year    = {2026}
}

Boundary-CB and Density-CB — two plug-and-play loss modules that resolve geometric ambiguity in long-tailed industrial point cloud segmentation. Achieves 55.74% mIoU on Industrial3D with a 21.7% relative gain on tail classes (reducer: 0% → 21.12%).