COURSE

ROAS6000J

Learning for 3D vision

Systems Hub / Robotics and Autonomous Systems · 3 Credits

Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods. In particular, this course will cover topics including - • Explicit, Implicit, and Neural 3D Representations • Differentiable Rendering • Single-view 3D Prediction: Objects, Scenes, and Humans • Neural Rendering • Multi-view 3D Inference: Radiance Fields, Multi-plane Images, Implicit Surfaces, etc. • Generative 3D Models • Shape Abstraction • Mesh and Point cloud processing