I am interested in estimating 3D scene representations from multi-view video sequences. In particular, I focus on combining semantic segmentation, object detection and classification with 3D reconstruction using efficient inference methods.
Perception is a fundamental part of intelligence since perception is necessary to acquire knowledge and knowledge is necessary to understand perception. Therefore computer vision is one of the most important aspects in the realization of intelligent systems. My interest of research lies in computer vision and the combination with machine learning which, to my mind, will enable the realization of intelligent systems. Currently, I am working on optical flow and how to incorporate high-level information to alleviate this ill-posed problem.
Human brains can learn from data without an obvious supervision target and can afterwards use this knowledge in a variety of situations and even generate new content. On the other hand, the applicability of modern supervised machine learning algorithms is limited by the amount of available training data. Generative probabilistic models seem to provide a promising way to tackle this problem and in addition allow us to incorporate knowledge of the underlying process. My long term goal is to use such models to tackle ambiguities in 3D reconstruction due to non-Lambertian materials and to reason about global illumination.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems