Segment Anything with Multiple Modalities
Aoran Xiao*, Weihao Xuan*, Heli Qi, Yun Xing, Naoto Yokoya, Shijian Lu (*Equal contribution)
arXiv, 2024.
[paper] [project]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]