Fall 2021 - CMPT 764 G100
Geometric Modelling in Computer Graphics (3)
Class Number: 5616
Delivery Method: In Person
Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Students with credit for CMPT 464 or equivalent may not take this course for further credit.
This course covers recent and advanced modeling techniques in computer graphics. Our focus will be on the acquisition, representation, processing, and synthesis of 3D shapes, with applications to real-time 3D graphics such as computer games, design and manufacturing, AR/VR, as well as 3D machine vision and robotics. The main modeling primitive studied will be polygonal meshes, which have been the dominant surface representation for highly detailed free-form 3D data. In recent years, mesh modeling and processing has been the most intensely studied subject in geometric modeling. This field is still fast evolving with many interesting problems and much aspiration for application development and future research, e.g., in geometric deep learning, computational design, and fabrication. Basic mathematical concepts and tools necessary to understand the course will be presented depending on students background. But the ability to program in C/C++ with OpenGL is required. This course will be cross-listed with CMPT 764.
COURSE-LEVEL EDUCATIONAL GOALS:
- The new computer graphics in the age of AI and Big Data
- Geometric modelling and 3D content creation
- 3D shape reps: tensor-product surfaces, implicits, solids, subdivision, point-sampled geometry
- 3D shape acquisition and surface reconstruction
- Digital shape processing and analysis: smoothing, feature extraction, segmentation, correspondence
- Level of details and multi-resolution modelling
- Machine learning in shape analysis and geometric modelling
- 3D printing
Two midterms (35%), two homework assignments (30%), and a final project (35%)
MATERIALS + SUPPLIES:
- Polygon Mesh Processing, Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez,and Bruno Levy, AK Peters, 2010, 9781568814261, 3rd Ed.
- Practical Algorithms for Image Analysis: Description, Examples & Code, M. Seul, L. O'Gorman, M. J. Sammon, Cambridge University Press, 2000, 9780521660655
- Tutorials on Multiresolution in Geometric Modelling, A. Iske, E. Quak, and M. S. Floater, Springer, 2002, 9783540436393
- Computer Graphics using Open GL, F. S. Hill Jr. and S. M. Kelley, Prentice Hall, 2007, 9780131496705
- Level of Detail for 3D Graphics, D. Luebke, et al, Morgan Kaufmann, 2003,
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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TEACHING AT SFU IN FALL 2021
Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place. Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.