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Aoran XIAO (肖傲然)
Full Professor
School of Electronics and Information
Harbin Institute of Technology, China
Email: aoran.xiao [at] hit.edu.cn
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Short Bio
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I am a Full Professor in the School of Electronics and Information at the Harbin Institute of Technology, China. My research lies at the intersection of 2D/3D computer vision, remote sensing interpretation, and multimodal foundation models. Broadly, I aim to develop intelligent systems capable of understanding the spatial–temporal world from diverse perspectives, platforms, and sensing modalities.
Previously, I was a research fellow at Nanyang Technological University (NTU), Singapore, working with Prof. Shijian Lu, and at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), Japan, collaborating with Prof. Naoto Yokoya. I received my Ph.D. from the College of Computing and Data Science at NTU under the supervision of Prof. Shijian Lu. I obtained my Master’s degree from State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, advised by Prof. Deren Li and Prof. Ruizhi Chen, and my Bachelor’s degree from Wuhan University.
I am always looking for highly motivated PhD and Master’s students. I also welcome collaborations and research discussions. Feel free to contact me.
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News
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[2026-02] I am ICME 2026 Area Chair.
[2026-02] Three papers are accepted to CVPR2026.
[2026-01] One paper is accepted to TGRS.
[2025-09] One paper is accepted to TGRS.
[2025-06] One paper is accepted to ICCV2025.
[2025-06] Our survey for remote sensing foundation models is accepted to IEEE GRSM (IF=16.2).
[2025-04] I am NeurIPS 2025 Area Chair.
[2025-01] I am co-organizing workshop on PixFoundation in CVPR 2025.
[2024-07] Our CAT-SAM is accepted to ECCV2024 as oral presentation.
[2024-06] Our survey for label-efficient learning of 3D point clouds is accepted to TPAMI!
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Selected Publications
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Foundation Models for Remote Sensing and Earth Observation: A Survey
Aoran Xiao, Weihao Xuan, Junjue Wang, Jiaxing Huang, Dacheng Tao, Shijian Lu, Naoto Yokoya
Accepted to IEEE Geoscience and Remote Sensing Magazine (GRSM), 2025.
[paper] [project]
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Segment Anything with Multiple Modalities
Aoran Xiao*, Weihao Xuan*, Heli Qi, Yun Xing, Naoto Yokoya, Shijian Lu (*Equal contribution)
arXiv, 2024.
[paper] [project]
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CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model
Aoran Xiao*, Weihao Xuan*, Heli Qi, Yun Xing, Ruijie Ren, Xiaoqin Zhang, Ling Shao, Shijian Lu (*Equal contribution)
European Conference on Computer Vision (ECCV), 2024. (Oral paper)
[paper] [project]
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A Survey of Label-Efficient Deep Learning for 3D Point Clouds
Aoran Xiao, Xiaoqin Zhang, Ling Shao, Shijian Lu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2024.
[paper] [project]
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Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial Consistency
Aoran Xiao, Dayan Guan, Xiaoqin Zhang, Shijian Lu
IEEE Transactions on Multimedia (T-MM), 2024.
[paper]
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Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Yun Xing, Jian Kang, Aoran Xiao, Jiahao Nie, Ling Shao, Shijian Lu
Advances in Neural Information Processing Systems (NeurIPS), 2023.
[paper] [project]
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3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric Xing.
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.
[paper] [project]
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Unsupervised Representation Learning for Point Clouds with Deep Neural Networks: A Survey
Aoran Xiao*, Jiaxing Huang*, Dayan Guan, Xiaoqin Zhang, Shijian Lu (*Equal contribution).
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023.
[paper] [project]
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PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Aoran Xiao, Jiaxing Huang, Dayan Guan, Kaiwen Cui, Shijian Lu, Ling Shao.
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[paper] [project]
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Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao.
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2022.
[paper] [project]
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Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation
Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu, Ling Shao.
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2022.
[paper] [project]
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Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, Shijian Lu.
AAAI Conference on Artificial Intelligence (AAAI), 2022.
[paper] [project]
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Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
Advances in Neural Information Processing Systems (NeurIPS), 2021.
[paper] [project]
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RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu
International Conference on Computer Vision (ICCV), 2021.
[paper] [project]
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Domain Adaptive Video Segmentation via Temporal Consistency Regularization
Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu
International Conference on Computer Vision (ICCV), 2021.
[paper] [project]
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Cross-View Regularization for Domain Adaptive Panoptic Segmentation
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao.
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2021.
[paper] [project]
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FSDR: Frequency Space Domain Randomization for Domain Generalization
Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao.
IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2021.
[paper]
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FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation
Aoran Xiao, Xiaofei Yang, Shijian Lu, Dayan Guan, Jiaxing Huang.
ISPRS journal of Photogrammetry and Remote Sensing, 2021.
[paper] [project]
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Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu, Yanpeng Cao.
IEEE Transactions on Multimedia (T-MM), 2021.
[paper] [project]
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Released Datasets
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SynLiDAR: A Large-Scale Synthetic LiDAR Point Cloud Dataset with Point-Wise Annotations
SynLiDAR is a large-scale synthetic LiDAR sequential point cloud dataset with point-wise annotations. 13 sequences of LiDAR point cloud with around 20k scans (over 19 billion points and 32 semantic classes) are collected from virtual urban cities, suburban towns, neighborhood, and harbor.
[paper] [project] [download]
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SemanticSTF: An Adverse-Weather LiDAR Point Cloud Dataset
SemanticSTF is a large-scale adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3D semantic segmentation under various adverse weather conditions.
[paper] [project] [download]
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Academic Service
Area Chairs
Workshops & Tutorials
Conference Reviews
- NeurIPS, CVPR, ICCV, ECCV, ICML, ICLR, ACCV
Journal Reviews
- TPAMI, TIP, TIV, TMM, IJCV, TCSVT, GRSM, ISPRS Journal of Photogrammetry and Remote Sensing, TGRS, Pattern Recognition
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