報告題目：Learning to Recover 3D Scene Shape from Single monocular Images
報告人：沈春華教授 阿德萊德大學（The University of Adelaide）
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. We investigate this problem in detail, and propose 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 us to recover a realistic 3D scene shape. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to enhance depth prediction models trained on mixed datasets. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot dataset generalization.
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. He has supervised over 20 PhD students to completion. His student alumni include two Australian Research Council DECRA fellows and additional graduates who are now in tenured or tenure track roles in Universities including the University of Adelaide, Sydney University, Monash University, Wollongong University, Nanyang Technological University Singapore, and quite a few other universities in China.