This reprostory is build for anyone who wants to do research about 3D point cloud.

If you have some suggestions, please contact [email protected]. Thanks for your valuable contribution.

http://www.cwang93.net/

http://3s.whu.edu.cn/ybs/index.htm

研究小组综合利用3D计算机视觉、深度学习、摄影测量与遥感等技术手段，实现基于多源异构大数据（如：点云、影像、矢量、模型、物联网数据）的城市空间智能， 服务于高清地图、道路基础设施数字化及健康状况检测、5G信号仿真及优化选址、文化遗产保护、能源-环境-生态仿真与预测、森林资源调查、电力线安全诊断等领域。 研究方向包括：（1）点云智能处理与深度学习（2）地理空间智能与GIS应用 （3）激光扫描测量与无人机摄影测量。

http://www.escience.cn/people/zhangliqiang/index.html

http://cfcs.pku.edu.cn/baoquan/

General Interests: Computer Graphics and Visualization Specific Interests: Large Environment Acquisition, Accelerated Rendering, Antialiasing, Volume Visualization Applications: Scientific & Biomedical Visualization, Digital Architecture Design, Art and Entertainment

http://cret.cnu.edu.cn/szdw/js/79569.htm

研究小组主要研究方向为：遥感卫星设计与数据预处理、多传感器激光雷达集成与后处理、深度学习与计算机视觉

http://lidar.radi.ac.cn/

中科院遥感与数字地球研究所激光雷达遥感团队于2009年10月由王成研究员创建，专注激光雷达遥感机理、激光雷达植被参数反演、激光雷达三维建模以及其他各种应用。

http://cret.cnu.edu.cn//szdw/js/63076.htm

研究小组主要研究方向为：高光谱影像，点云硬件软件开发

http://vision.ia.ac.cn/zh/index_cn.html

研究组主要研究方向为基于视觉影像的三维场景重建，视觉定位与姿态估计，机器人视觉导航等，同时将计算机视觉技术应用到**古代文物修复、采矿区域边坡三维重建 航拍影像自动地形生成等领域。

http://cloud.eic.hust.edu.cn:8071/~xbai/

研究组主要研究方向为分割和形状表示。涵盖3D视觉，多传感器融合+

https://engineering.purdue.edu/CE/Academics/Groups/Geomatics/DPRG

http://www.cs.hunter.cuny.edu/~ioannis/

https://scholar.google.com/citations?hl=en&user=ENLTjooAAAAJ&view_op=list_works&sortby=pubdate

https://graphics.usc.edu/cgit/index.php

The fields of computer graphics, computer vision, and immersive technologies all fall within the lab's scope. Activities in the lab range from fundamental algorithms and mathematical methods to systems and application prototypes. Students engage in interdisciplinary research driven by realistic needs and problems. Unique domain knowledge is obtained through the laboratory's twenty-year history of close ties to multidisciplinary centers such as CiSoft (Center for Interactive Smart Oilfield Technologies) and IMSC (Integrated Media Systems Center).

https://ai4ce.github.io/

http://www.ece.lsu.edu/xinli/

Geometric Data Processing Large-scale Visual (Geometric/Image/Spatiotemporal) Data Modeling and Understanding Computer Graphics, Computer Vision, Image Processing and Analysis

https://geometry.stanford.edu/member/guibas/
https://geometry.stanford.edu/member/rqi/

Professor Guibas heads the Geometric Computation group in the Computer Science Department of Stanford University. He is acting director of the Artificial Intelligence Laboratory and member of the Computer Graphics Laboratory, the Institute for Computational and Mathematical Engineering (iCME) and the Bio-X program. His research centers on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. Professor Guibas' interests span computational geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics, and discrete algorithms --- all areas in which he has published and lectured extensively.

https://cseweb.ucsd.edu/~haosu/

PointNet, PointNet++

http://caor-mines-paristech.fr/en/research/point-cloud-and-3d-modeling-pc3dm/

Our overall objective is the computerized geometric modeling of complex scenes from physical measurements. On the geometric modeling and processing pipeline, this objective corresponds to steps required for conversion from physical to effective digital representations: analysis, reconstruction and approximation. The related scientific challenges include i) being resilient to defect-laden data due to the uncertainty in the measurement processes and imperfect algorithms along the pipeline, ii) being resilient to heterogeneous data, both in type and in scale, iii) dealing with massive data, and iv) recovering or preserving the structure of complex scenes. We define the quality of a computerized representation by its i) geometric accuracy, or faithfulness to the physical scene, ii) complexity, iii) structure accuracy and controllability, and iv) amenability to effective processing and high level scene understanding.

http://www.ipb.uni-bonn.de/people/cyrill-stachniss/

Research Interests Probabilistic Robotics Localization, Mapping, SLAM, Bundle Adjustment Autonomous Navigation and Exploration Visual and Laser Perception Scene Analysis and Classification Robotics for Agriculture Unmanned Aerial Vehicles (UAVs and MAVs) Autonomous Cars, Logistics, Wheeled Robots, …

https://cg.cs.uni-bonn.de/en/people/prof-dr-reinhard-klein/

https://www.ifp.uni-stuttgart.de/en/institute/

http://www.pf.bgu.tum.de/

https://yweirt.github.io/

https://www2.cs.sfu.ca/~furukawa/

https://yang7879.github.io/

研究方向主要包括：（1）实例和语义分割（2）machine learning, computer vision, and robotics

https://www.ucl.ac.uk/civil-environmental-geomatic-engineering/people/dr-jan-boehm

Jan Boehm's research focuses on photogrammetry, image understanding and robotics. With his background in Computer Science he wants to bridge the remaining gap between photogrammetry and computer vision. The latter provides key components to increased productivity in the geomatic processing pipeline. In past projects he already successfully leveraged the productivity in terrestrial laser scanning by introducing automation to georeferencing by direct georeferencing, automated registration using intensity features and automated modelling strategies.

https://research.utwente.nl/en/persons/george-vosselman

https://www.adelaide.edu.au/aiml/our-research

https://people.eng.unimelb.edu.au/kkhoshelham/people.html

https://prs.igp.ethz.ch/

Semantic3D Datase

Computer Vision and Geometry Group(CVCG) https://cvg.ethz.ch/people/faculty/ Interactive Geometry Lab(IGL) https://igl.ethz.ch/ Computer Graphics Laboratory(CGL) https://cgl.ethz.ch/people/faculty.php

https://sites.google.com/site/gimheelee/

Computer Vision: Minimal Problems, 3D Computer Vision, Structure-from-Motion (SfM), Visual Place Recognition, 3D Human Pose Prediction/Estimation, 3D Object Detection, and Scene Understanding. Robotic Perception: Self-driving car, Micro-Aerial Vehicles (MAVs), Visual Odometry, and Simultaneous Localization and Mapping (SLAM). Machine Learning: Deep Learning and Probabilistic Graphical Modeling.

http://www.vision.deis.unibo.it/

SHOT

http://geotech.webs.uvigo.es/index.html

https://scholar.google.es/citations?hl=es&user=ukRzH4oAAAAJ&view_op=list_works&sortby=pubdate

The Applied Geotechnologies Group is a research team at the University of Vigo that develops, tests and applies geo-technologies for problem solving in different fields such as environment, cultural heritage, terrestrial and coastal infrastructures and architecture. Nowadays, the main research lines include infrastructure management systems and energy efficiency studies based on as-built BIM reconstruction. The group was recently recognized as Galician reference group because of the quality of the scientific publications and the number of successful R&D projects funded by external agents.