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Prof. Chunhua Shen Gave an Online Talk at CRIPAC

As part of CRPAC’s Lecture Series in Intelligent Perception and Computing, Prof. Chunhua Shen was invited to give an online talk on Friday, 2nd April 2021. Prof. Shen is a full professor of computer science at the University of Adelaide, and his research mainly focuses on Machine Learning and Computer Vision.

Prof Shen’s talk was entitled “Learning to Recover 3D Scene Shape from Single monocular Images”, and the talk was hosted by Prof. Zhaoxiang Zhang from CRIPAC. Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. Prof. Shen’s group investigated this problem in detail and proposed a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then use 3D point cloud encoders to predict the missing depth shift and focal length that allow to recover a realistic 3D scene shape. In addition, they proposed an image-level normalized regression loss and a normal-based geometry loss to enhance depth prediction models trained on mixed datasets. The method was tested on nine unseen datasets and achieved state-of-the-art performance on zero-shot dataset generalization.

Prof Shen’s report was wonderful and attracted many students to participate online. This talk also brought new inspiration for our future research directions and methods.

 

Biography of Prof. Chunhua Shen:

Professor Shen has been a Full Professor of Computer Science at The University of Adelaide since 2014, and Founding Director of the Machine Learning Theory theme at the Australian Institute for Machine Learning. His research mainly focuses on Machine Learning and Computer Vision. He was recognised as a top 5 researcher for Engineering and Computer Sciences as part of The Australian’s Life time Achievement Leaderboard (Sept. 2020, https://specialreports.theaustralian.com.au/1540291/9/), which is an exceptional achievement.

 
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