Perception systems form a crucial part of autonomous and artificial intelligence systems since they convert data about the relationship between an autonomous system and its environment into meaningful information. This information is then processed to produce optimal actions towards achieving desired goals. Perception systems can be difficult to build since they may involve modeling complex physical systems or other autonomous agents. In such scenarios, data driven models may be used to augment physics based models for perception. In this talk, I will present work making use of data driven models for perception tasks, highlighting the benefit of such approaches for autonomous systems. I will present work with application to pedestrian perception for autonomous vehicles and 3D shape and pose estimation for indoor scene understanding and reconstruction.
Oladapo (Dapo) Afolabi is a final year PhD candidate in Professor Shankar Sastry's lab in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley. He received his B.S. in Electrical Engineering from the University of Virginia in 2014. He is broadly interested in problems involving computer vision and autonomous systems. He has worked in areas such as Autonomous Driving, Energy Disaggregation and 3D reconstruction. His recent focus has been on improving computer vision based 3D reconstruction and understanding for indoor scenes, making use of geometric properties of objects as well as deep generative models to build 3D models that are amenable to Mixed Reality applications.