Talk Abstract:  Neural 3D scene representations have had a significant impact on computer vision, seemingly freeing deep learning from the shackles of large and curated 3D datasets. However, many of these techniques still have strong assumptions that make them challenging to build and consume for the average user. During this talk, I will question some of these assumptions. Specifically, we will remove the requirement for multiple calibrated images of the same scene (LoLNeRF), eliminate the necessity for the scene to be entirely static during capture (RobustNeRF), and enable the inspection of these models using consumer-grade mobile devices, rather than relying on high-end GPUs (MobileNeRF).

Speaker Biography:   Andrea Tagliasacchi is an associate professor at Simon Fraser University (Vancouver, Canada) where he holds the appointment of “visual computing research chair” within the School of Computing Science. He is also a part-time (20%) staff research scientist at Google Brain (Toronto), as well as an associate professor (status only) in the Computer Science Department at the University of Toronto. Before joining SFU, he spent four wonderful years as a full-time researcher at Google (mentored by Paul Lalonde, Geoffrey Hinton, and David Fleet). Before joining Google, he was an assistant professor at the University of Victoria (2015-2017), where he held the Industrial Research Chair in 3D Sensing (jointly sponsored by Google and Intel). His alma mater include EPFL (postdoc) SFU (PhD, NSERC Alexander Graham Bell fellow) and Politecnico di Milano (MSc, gold medalist). His research focuses on 3D visual perception, which lies at the intersection of computer vision, computer graphics and machine learning. 

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