HKUCS Computer Vision Lab Virtual Workshop Series
When 3D reconstruction meets deep learning

Host: Dr. Kenneth K.Y. Wong, University of Hong Kong

Date: 07 Jan 2021

Time: 10:30am - 1:50pm
(GMT+8, HK Time)

Webinar via Zoom


3D reconstruction refers to the process of recovering 3D shape of objects from sensor data. It has always been one of the key topics in computer vision, and has broad applications in robotics, visualization, entertainment, healthcare, etc. Over the past 5 decades, many sophisticated methods have been proposed based on physical and geometric constraints, and the reconstruction problem is once considered largely solved. With the recent success of deep learning in many computer vision tasks, researchers are interested in revisiting the 3D reconstruction problem and deriving novel learning based methods to achieve new standards in reconstruction quality and robustness. This workshop invites internationally renowned experts to share their findings and insights in deep learning based 3D reconstruction with the research community.


HK Time (GMT+8)Title of TalkSpeaker
10:30am – 10:35amOpening remarks by Dean of EngineeringProf. Christopher Chao
The University of Hong Kong, HK
10:35am – 11:10amTalk 1 : The Plenoptic CameraProf. Noah Snavely
Cornell University, USA
11:10am – 11:45amTalk 2 : Making Avatars and Volumetric Teleportation Accessible
using 3D Deep Learning
Prof. Hao Li
Pinscreen and UC Berkeley, USA
11:45am – 11:55amBreak
11:55am – 12:30pmTalk 3 : End-to-End Learnable Geometric VisionProf. Tat-Jun Chin
The University of Adelaide, Australia
12:30pm – 1:05pmTalk 4 : Learning to Reconstruct Whole Expressive 3D
Human Pose and Shape from Single Image
Prof. Kyoung Mu Lee
Seoul National University, Korea
1:05pm – 1:15pmBreak
1:15pm – 1:50pmTalk 5 : Neural Implicit Representations for 3D VisionProf. Andreas Geiger
University of Tübingen, Germany


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