Varun Jampani: Practical 3D Object Understanding from Image Collections and Videos

This talk was held on February 10, 2022 as a part of the MLFL series, hosted by the Center for Data Science, UMass Amherst.

Abstract: Much of computer vision is understanding objects around us. In this talk, I will give an overview of our research works on understanding 3D object properties from 2D image collections and videos. Annotating several of the 3D object properties such as 3D shape, material properties, etc. is very labor-intensive and can not easily scale to large-scale datasets and new object categories. So, techniques that can estimate these object properties with minimal supervision are important for practical purposes.

Bio: Varun Jampani is a researcher at Google Research in Cambridge, US. Prior to that, he was a researcher at NVIDIA. He works in the areas of machine learning and computer vision and his main research interests include content-adaptive neural networks, self-supervised visual discovery and novel view synthesis. He obtained his PhD with highest honors at Max Planck Institute for Intelligent Systems (MPI) and the University of Tübingen in Tübingen, Germany. He obtained his BTech and MS from the International Institute of Information Technology, Hyderabad (IIIT-H), India, where he was a gold medalist. His work on 'SplatNet' has received 'Best Paper Honorable Mention' award at CVPR'18.

About Machine Learning and Friends Lunch: MLFL is a lively and interactive forum held weekly where friends of the UMass Amherst machine learning community can sit down, have lunch, and give or hear a 50-minute presentation on recent machine learning research. This semester of the UMass MLFL series has been graciously sponsored by our friends at Oracle Labs.

Please follow this link to know more about the past and upcoming talks: http://umass-mlfl.github.io