Publications - Bo Qiu

Cross-modal semantic transfer for point cloud semantic segmentation

Zhen Cao*, Xiaoxin Mi*, Bo Qiu, Zhipeng Cao, Chen Long, Xinrui Yan, Chao Zheng, Zhen Dong†, Bisheng Yang (* equal contribution)

ISPRS Journal of Photogrammetry and Remote Sensing (SCI, JCR Q1, IF=12.7, TOP Journal in Geosciences) 2025

This study introduces a superpoint-assisted Neural Radiance Field (NeRF) for point cloud semantic segmentation without requiring additional training data. For a given scene, the proposed method uses point clouds, associated images, and image pseudo-semantic labels as inputs to achieve pointwise semantic segmentation.

[paper] [github]

Cross-modal semantic transfer for point cloud semantic segmentation

Zhen Cao*, Xiaoxin Mi*, Bo Qiu, Zhipeng Cao, Chen Long, Xinrui Yan, Chao Zheng, Zhen Dong†, Bisheng Yang (* equal contribution)

ISPRS Journal of Photogrammetry and Remote Sensing (SCI, JCR Q1, IF=12.7, TOP Journal in Geosciences) 2025

This study introduces a superpoint-assisted Neural Radiance Field (NeRF) for point cloud semantic segmentation without requiring additional training data. For a given scene, the proposed method uses point clouds, associated images, and image pseudo-semantic labels as inputs to achieve pointwise semantic segmentation.

[paper] [github]

Ubiquitous Point Cloud: Theory, Model, and Applications (1st ed.)

Bisheng Yang, Zhen Dong, Fuxun Liang, Xiaoxin Mi, et al.

CRC Press 2024

This book provides the latest theory and methodology for point cloud processing with Al to better serve earth observation, 3D vision, autonomous driving, smart city, and geospatial information applications. (I was responsible for writing Chapter 7 of this book, titled Point Cloud Semantic Segmentation.)

[eBook]

Ubiquitous Point Cloud: Theory, Model, and Applications (1st ed.)

Bisheng Yang, Zhen Dong, Fuxun Liang, Xiaoxin Mi, et al.

CRC Press 2024

This book provides the latest theory and methodology for point cloud processing with Al to better serve earth observation, 3D vision, autonomous driving, smart city, and geospatial information applications. (I was responsible for writing Chapter 7 of this book, titled Point Cloud Semantic Segmentation.)

[eBook]

WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation

Bo Qiu*, Yuzhou Zhou*, Lei Dai, Bing Wang, Jianping Li, Zhen Dong†, Chenglu Wen, Zhiliang Ma, Bisheng Yang (* equal contribution)

IEEE TITS (In Press, SCI, JCR Q1, IF=7.9, TOP Journal in Engineering Technology) 2024

This paper introduces WHU-Railway3D, a diverse point cloud semantic segmentation (PCSS) dataset specifically tailored for railway scenes. Spanning approximately 30 km and comprising 4.6 billion points, the dataset includes 11 richly annotated categories across urban, rural, and plateau railway environments.

[github] [paper]

WHU-Railway3D: A Diverse Dataset and Benchmark for Railway Point Cloud Semantic Segmentation

Bo Qiu*, Yuzhou Zhou*, Lei Dai, Bing Wang, Jianping Li, Zhen Dong†, Chenglu Wen, Zhiliang Ma, Bisheng Yang (* equal contribution)

IEEE TITS (In Press, SCI, JCR Q1, IF=7.9, TOP Journal in Engineering Technology) 2024

This paper introduces WHU-Railway3D, a diverse point cloud semantic segmentation (PCSS) dataset specifically tailored for railway scenes. Spanning approximately 30 km and comprising 4.6 billion points, the dataset includes 11 richly annotated categories across urban, rural, and plateau railway environments.

[github] [paper]

RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation

Tengping Jiang, Bisheng Yang†, Yongjun Wang†, Lei Dai, Bo Qiu, Shan Liu, Shiwei Li, Qinyu Zhang, Xin Jin, Wenjun Zeng

IEEE TGRS (SCI, JCR Q1, IF=7.5) 2023

This study introduces RailSeg, a deep learning framework for railway point cloud semantic segmentation, focusing on point cloud downsampling, integrated local–global feature extraction, spatial context aggregation, and semantic regularization.

[paper]

RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation

Tengping Jiang, Bisheng Yang†, Yongjun Wang†, Lei Dai, Bo Qiu, Shan Liu, Shiwei Li, Qinyu Zhang, Xin Jin, Wenjun Zeng

IEEE TGRS (SCI, JCR Q1, IF=7.5) 2023

This study introduces RailSeg, a deep learning framework for railway point cloud semantic segmentation, focusing on point cloud downsampling, integrated local–global feature extraction, spatial context aggregation, and semantic regularization.

[paper]