We are Urban Spatial Intelligence (USI) Research Group at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University. We focus on 3D Computer Vision, particularly including 3D reconstruction, scene understanding, and point cloud processing as well as their applications in intelligent transportation system, digital twin cities, urban sustainable development, and robotics. Check our works by topic:
Our Team (click to expand):
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Lab Leaders
Name Role Bisheng Yang Professor, Head of LIESMARS, Wuhan University Zhen Dong Professor, Head of 3S Integration, Wuhan University Chi Chen Associate Professor, Wuhan University -
Academic Advisors
Name Role Yuan Liu Assistant Professor, Hong Kong University of Science and Technology Bing Wang Assistant Professor, Hong Kong Polytechnic University Wenxia Dai Associate Professor, China University of Geosciences(Wuhan) Jianping Li PostDoc, Nanyang Technological University Fuxun Liang PostDoc, Wuhan University Xiaoxin Mi PostDoc, Wuhan University of Technology Ningning Zhu PostDoc, Wuhan University -
Active Members
Name Role Research Interests Yuhao Li Ph.D. student, Wuhan University LiDAR SLAM, Multi-modality Fusion Xianghong Zou Ph.D. student, Wuhan University Point Cloud Localization, 3D Change Detection Haiping Wang Ph.D. student, Wuhan University 3D Reconstruction / Understanding / LLM Zhe Chen Ph.D. student, Wuhan University Scene Understanding, 3D Urban Morphology Xin Zhao Ph.D. student, Wuhan University Robot Mapping, LiDAR SLAM, Localization Chen Long Ph.D. student, Wuhan University PC Enhancement, Urban Sustainable Development Zhen Cao Ph.D. student, Wuhan University PC Completion, Scene Understanding Luqi Zhang Ph.D. student, Wuhan University 3D change detection / point cloud segmentation Chong Liu Ph.D. student, Wuhan University Scene Understanding, Intelligent Transportation Xiaochen Yang Ph.D. student, Wuhan University Point Cloud Registration Bo Qiu M.S. student, Wuhan University Scene Understanding, intelligent transportation systems Youqi Liao M.S. student, Wuhan University Visual Localization, Place Recognition Hang Xu M.S. student, Wuhan University Point Cloud Generation / Completion / Editing Chengjie Li M.S. student, Wuhan University 3D City / Building reconstruction, Urban Morphology & Microclimate Yuning Peng M.S. student, Wuhan University 3D Reconstruction / Understanding / LLM Yizhe Zhang M.S. student, Wuhan University Robotics, 3D Reconstruction, Automatic Control Qingwen Tan M.S. student, Wuhan University Semantic Segmentation, Diffusion Models
Public datasets (click to expand):
- 📂 WHU-TLS
: TLS PC registration benchmark covering 11 scenarios;
- 📂 WHU-Helmet
: A helmet-based multi-sensor SLAM benchmark;
- 📂 WHU-Urban-3D : ALS/MLS semantic/instance segmentation benchmark;
- 📂 WHU-Railway3D
: Semantic segmentation benchmark for railway scenario;
- 📂 WHU-Lane
: A Benchmark Approach and Dataset for Large-scale Lane Mapping from MLS Point Clouds;
Point Cloud Registration (click to expand):
- 📂 BSC (ISPRS J'17)
: A handcrafted point cloud local descriptor utilizing CPU;
- 📂 YOHO (ACM MM'22)
: A learning-based point cloud local rotation-equivariant descriptor;
- 📂 RoReg (TPAMI'23)
: Utilizing rotation-equivariance in the whole pipeline of pairwise registration;
- 📂 SGHR (CVPR'23)
: A simple multiview pc registration baseline;
- 📂 MSReg (IEEE TGRS'24)
: Fast 4DOF registration of MLS and stereo point clouds;
Image-to-point cloud Registration (click to expand):
- 📂 FreeReg (ICLR'24)
: FreeReg extracts cross-modality features from pretrained diffusion models and monocular depth estimators for accurate zero-shot image-to-point cloud registration;
- 📂 CoFiI2P (RA-L'24)
: CoFiI2P is a coarse-to-fine framework for image-to-point cloud registration task;
3D Generation (click to expand):
- 📂 VistaDream (arXiv'24)
: VistaDream is a training-free framework to reconstruct a high-quality 3D scene from a single-view image;
Point Cloud Upsampling (click to expand):
- 📂 PC2-PU (ACM MM'22)
: A transformer-based point cloud upsampling baseline;
Point Cloud / Depth Completion (click to expand):
- 📂 KT-Net (AAAI'23)
: A transformer-based point cloud completion baseline;
- 📂 SparseDC (Information Fusion'24)
: Depth Completion from sparse and non-uniform inputs;
- 📂 EGIInet (ECCV'24)
: Single view image guided point cloud completion framework;
Visual Localization (click to expand):
- 📂 PatchAugNet (ISPRS J'23)
: A cross-platform pc localization baseline;
- 📂 LAWS (ISPRS J'24)
: Regard point cloud localization as a classification problem;
- 📂 OSMLoc (arXiv'24)
: An image-to-OpenstreetMap (I2O) visual localization framework;
Normal Estimation (click to expand):
- 📂 AdaFit (ICCV'21)
: Rethinking pc normal estimation;
Image / 3D Understanding (click to expand):
- 📂 ME-Net (JAG'23)
: Objection detection utilizing both image and Lidar from mobile platform;
- 📂 Mobile-Seed (RAL'24)
: An online framework for simultaneous semantic segmentation and boundary detection on compact robots;
- 📂 GAGS (arXiv'24)
: GAGS enables accurate open-vocabulary understanding of a 3D scene;
Urban Morphology & Sustainable Development (click to expand):
- 📂 3DBIE-SolarPV (Applied Energy‘24)
: City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China;
HDMap (click to expand):
- 📂 LaneMapping
