Welcome to the course!
Please sign up with Piazza
General InformationTimes & Places
WF 1:30PM - 2:50PM, Gates 392
|Instructor||Leonidas J. Guibasfirstname.lastname@example.org||Tue, 1:30-2:30pm
(not on 4.18, 4.25, 5.2)
|Instructor||Anastasia Dubrovinaemail@example.com||Wed, Fri 4:30-5:30pm
(send me an email if
you plan to come)
|Course Assistant||Lin Shaofirstname.lastname@example.org||Mon, Thu, 10:00-11:30am||Clark S250|
ObjectivesThis 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.
PrerequisitesBackground assumed includes basic material in computer graphics, linear algebra, machine learning and optimization.
- Homeworks (3 assignments) 60%
- Final project 40%
- The course staff would like to thank the Stanford Computer Forum for their support.
- Many of the lectures are based on the lecture slides from the Data Driven Shape Analysis and Processing course, as well as various presentations by Qixing Huang, Vova Kim, Vangelis Kalogerakis, Kai Xu, Siddhartha Chaudhuri, and others. We would like to thank all authors for sharing their resources.