Machine Learning for 3D Data

CSE291-I00 - Winter 2018


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General Information

Times & Places
TuTh 3:30PM - 4:50PM, EBU3B 4140

Course Staff

Name Email Office Hours Location
Instructor Hao Su 2:00PM-3:00PM, Mo CSE Building 4114
Course Assistant Vignesh Gokul 5:00PM-6:30PM, MoFr CSE Building B250A
6:00PM-7:30PM, We CSE Building B270A


This course will explore the state of the art algorithms for both supervised and unsupervised machine learning on 3D data - analysis as well as synthesis. After a brief introduction to geometry foundations and representations, the focus of the course will be machine learning methods for 3D shape classification, segmentation, and symmetry detection, as well as new shape synthesis. Techniques for analyzing not only individual 3D models but entire collections of such through computing alignments, and maps or correspondences, will be discussed. Deep neural architectures appropriate for data in the form of point clouds or graphs will also be studied, as well as architectures that can associate semantic information with object models, including functionality. Finally generative models for 3D shape design will be covered, for example adaptations of generative adversarial networks (GANs). Data sources for the course include public 3D model repositories such a the Trimble 3D Warehouse or Yobi3D and semantic annotation knowledge bases such as ShapeNet.


Background assumed includes basic material in linear algebra, optimization, machine learning, and graphical models.

Grading (tentative)


The planned syllabus is as below. Certain contents may be added or removed based upon the interactions in class and other situations.