Learning 3D shape generation from 3D data and images

PhD Qualifying Examination


Title: "Learning 3D shape generation from 3D data and images"

by

Miss Jaeyeon KIM


Abstract:

With the advent of hardware, collecting data, deep learning models have been 
successfully used to solve a variety of computer vision and graphics problems. 
Especially in the 2D domain, intense deep generative models have been developed 
and demonstrated creative ability. Meanwhile, the demand for 3D creation is 
increasing from real-world applications that process 3D data. However, due to 
the unique challenges of 3D shape generation based on 3D understanding and the 
physical world, 3D shape generation is still in its early stages. This survey 
provides a comprehensive review of recent advances in deep learning methods for 
3D shape generation, which can be used to inspire future research.


Date:			Friday, 17 February 2023

Time:                  	3:30pm - 5:30pm

Venue:                  Room 4502
                         Lifts 25/26

Committee Members:	Dr. Sai-Kit Yeung (Supervisor)
  			Prof. Chi-Keung Tang (Chairperson)
 			Dr. Tristan Braud
 			Prof. Pedro Sander


**** ALL are Welcome ****