CMPSCI 791SU - S-MachLrng/ShapeUnderstanding

Institution:

University of Massachusetts Amherst

Subject:

Description:

The emergence of modern geometry acquisition devices, such as the Kinect, and the appearance of large-scale shape repositories, such as the Google Warehouse, are revolutionizing computer graphics, making three-dimensional content ubiquitous. The need for algorithms that understand and intelligently process 3D shapes is thus greater than ever. This seminar will provide a state-of-the-art overview of the field with particular emphasis on machine learning applications to shape understanding. The seminar will cover topics such as extraction of 3D geometric descriptors and symmetries from shapes, probabilistic models for shape segmentation, retrieval, and reconstruction, 3D template fitting, discovery of structural and contextual relationships of shapes in scenes, generative models of shapes, and applications to 3D modeling. Students read, present and critique state-of-the-art research papers on the above topics and learn how machine learning can be used for 3D shape analysis and synthesis. Familiarity with probability, statistics, and linear algebra is required. Experience with vision, graphics, or machine learning useful but not required.

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.umass.edu

Phone Number:

(413) 545-0111

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Semester

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