CS 6672 - 3D Vision

Fall. 3 credits. Student option grading.

Prerequisite: CS 2110, CS 2800, CS 4670, MATH 1920, MATH 2940 or their equivalents. Primarily for: PhD students.

W. Ma.

The ability to infer, model, and utilize 3D information from perceptual input is crucial to various intelligent systems (e.g., self-driving vehicles, mobile robots) and AI tasks (e.g., 2D image/3D asset generation, robot manipulation). The course will investigate the fundamentals and the latest advances in 3D vision as well as their applications in different fields. The topics include image formation, multi-view geometry, (neural) 3D representations, learning-based 3D algorithms, neural rendering, generative models, etc. The course will feature a mixture of lectures, paper presentations (both classic and modern), and a group final project. The students will play around various algorithms and models and improve or propose a creative use of them.

Outcome 1: Describe the challenges and limitations in 3D.

Outcome 2: Analyze the pros and cons of 3D techniques and properly benchmark them.

Outcome 3: Design new solutions to address identified limitations.

Outcome 4: Identify potential applications of different 3D algorithms and applying them to different domains to resolve respective challenges.