SONG, Jie - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Fall 2023-24 Summer 2023-24 Spring 2021-22 Winter
2024-25 Fall
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	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

VECTOR

[3-0-0:3]

DESCRIPTION

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

ROAS 6000J - Learning for 3D vision (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6180)	02-SEP-2024 - 06-DEC-2024 Tu 03:00PM - 05:50PM	Rm 202, E1	SONG, Jie	20	17	3	0	Instructor Consent Required