3D Point Cloud Processing
Instructor: Gady Agam, Illinois Institute of Technology
Level: Introductory
Duration: 4 Hours plus 30-minute break and 30-minute post-class discussion
Course Time:
    New York: Tuesday 12 January, 10:00 – 15:00
    Paris: Tuesday 12 January, 16:00 – 21:00
    Tokyo: Wednesday 13 January, 00:00 – 05:00

Benefits
This course enables the attendee to:

• Describe fundamental concepts for point cloud processing.
• Develop algorithms for point cloud processing.
• Incorporate point cloud processing in your applications.
• Understand the limitations of point cloud processing.
• Use industry standard tools for developing point cloud processing applications.

Point clouds are an increasingly important modality for imaging with applications ranging from user interfaces to street modeling for GIS. Range sensors such as the Intel RealSense camera or Microsoft Azure Kinect camera are becoming increasingly small and cost effective thus opening a wide range of applications. The purpose of this course is to review the necessary steps in point cloud processing and introduce fundamental algorithms in this area.

Point cloud processing is similar to traditional image processing in some sense yet different due to the 3D and unstructured nature of the data. In contrast to a traditional camera sensor which produces a 2D array of samples representing an image, a range sensor produces 3D point samples representing a 3D surface. The points are generally unorganized and so are termed “cloud”. Once the points are acquired there is a need to store them in a data structure that facilitates finding neighbors of a given point in an efficient way. The point cloud often contains noise and holes which can be treated using noise filtering and hole filling algorithms. For computational efficiency purposes the point cloud may be downsampled. In an attempt to further organize the points and obtain a higher level representation of the points, planar or quadratic surface patches can be extracted and segmentation can be performed. For higher level analysis key points can be extracted and features can be computed at their locations. These can then be used to facilitate registration and recognition algorithms. Finally, for visualization and analysis purposes the point cloud may be triangulated. The course discusses and explains the steps described above and introduces the increasingly popular PCL (Point Cloud Library) and other open source frameworks for processing point clouds.

Intended Audience
Engineers, researchers, and software developers, who develop imaging applications and/or use camera sensors for inspection, control, and analysis.

Gady Agam is an associate professor of computer science at the Illinois Institute of Technology. He is the director of the Visual Computing Lab at IIT which focuses on imaging, geometric modeling, and graphics applications. He received his PhD from Ben-Gurion University (1999).

COST

Discounts given for multiple classes. See Registration page for details and to register.

For office use only: