AI-PC: AI-based Point Cloud and Image Understanding



Recent rapid and revolutionary development of AI-based techniques has brought remarkably eye-catching and competitive achievements in a wide variety of point cloud and image data interpretation tasks including landmark recognition, semantic segmentation, change detection, etc. This workshop will see contributions the topics related the state of the art and future trends addressed the following topics: new point cloud and image algorithm designs, point clouds element detection and segmentation, sensor fusion algorithms, 3D LiDAR from airborne, terrestrial, mobile and backpack autonomous systems and applications, change detection from point cloud and image data, 3D real scene, deep learning methods for point cloud and image processing, etc.
The workshop will be part of the ISPRS Geospatial Week 2023 and is hosted by the Arab Academy for Science, Technology, and Maritime Transport (AASTMT) in parallel with several related geospatial workshops.


Themes of event:
  • 3D LiDAR processing
  • LiDAR and image fusion systems
  • AI-based methods on point cloud processing technologies
  • AI-based methods on image processing technologies
  • Point cloud and image detection
  • Visual Odometry and LiDAR 3D SLAM
  • Semantic Segmentation
  • Instance Segmentation
  • Elements extraction from LiDAR and image data
  • LiDAR altimetry
  • Deep learning algorithms based on LiDAR and image
  • Change detection
  • Benchmark dataset using point clouds and image
  • 3D LiDAR from autonomous systems and applications
  • UAV-based LiDAR and photogrammetry cloud points processing

Scientific Committee:
  • Jonathan Li, University of Waterloo, Canada
  • Leila Hashemi Beni, North Carolina A&T State University, USA
  • Yiping Chen, Sun Yat-sen University, China
  • Luis Miguel González de Santos, University of Vigo, Spain
  • Haiyan Guan, Nanjing University of Information Science and Technology, China
  • Samsung Lim, University of New South Wales, Australia 
  • Michael A. Chapman,Toronto Metropolitan University, Canada
  • Yuwei Chen, Finnish Geospatial Research Institute (FGI), Finland