Spring 2021 - CMPT 464 D100
Geometric Modelling in Computer Graphics (3)
Class Number: 7021
Delivery Method: Remote
Course Times + Location:
Mo 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
Th 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
1 778 782-6843
Prerequisites:CMPT 361, MACM 316.
Covers 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 469 between 2003 and 2007 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.
- 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
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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TEACHING AT SFU IN SPRING 2021
Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges 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. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112).