Advances in Computer Vision
Advanced topics in computer vision focusing on geometry in computer vision, including image formation, representation theory for vision, classic multi-view geometry, multi-view geometry in the age of deep learning, differentiable rendering, neural scene representations, correspondence estimation, optical flow computation, and point tracking. Topics include generative modeling and representation learning including image and video generation, guidance in diffusion models, conditional probabilistic models, as well as representation learning in the form of contrastive and masking-based methods. Explores the intersection of robotics and computer vision with "vision for embodied agents," investigating the role of vision for decision-making, planning and control.
Spring 2025
This course dives into advanced concepts in computer vision. A first focus is geometry in computer vision, including image formation, representation theory for vision, classic multi-view geometry, multi-view geometry in the age of deep learning, differentiable rendering, neural scene representations, correspondence estimation, optical flow computation, and point tracking. Next, we explore generative modeling and representation learning including image and video generation, guidance in diffusion models, and conditional probabilistic models, as well as representation learning in the form of contrastive and masking-based methods. Finally, we will explore the intersection of robotics and computer vision with "vision for embodied agents," investigating the role of vision for decision-making, planning and control.
Prerequisites & course information
Prereq: 6.7960 , ( 6.1200[J] or 6.3700 ), and ( 18.06 or 18.C06[J] ) G (Spring) 3-0-9 units Credit cannot also be received for 6.4300
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2025
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.