The Autonomous Vision Group is focused on 3D scene understanding, reconstruction, motion estimation, generative modeling and sensori-motor control in the context of autonomous systems. Our goal is to make artificial intelligent systems such as self-driving cars or household robots more autonomous, efficient, robust and safe. By making progress towards these goals, we also strive for uncovering the fundamental concepts underlying visual perception and autonomous navigation. Our research is guided by the following principles.
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We live in a three-dimensional world, thus understanding our world in 3D is important. A first step towards this 3D understanding is to accurately reconstruct our environment based on 2D image measurements or sparse 3D point clouds. We have developed novel representations, algorithms and benchmarks for this task. Read More
Intelligent systems as well as many computer vision tasks benefit from an accurate understanding of object motion as well as the interplay between scene elements. However, motion is even less constrained than geometric structure and obtaining ground truth is difficult. Furthermore, besides pure 2D image motion, syst... Read More
While deep learning undeniably achieves impressive results for numerous different computer vision tasks, the theoretical foundations behind the success of these methods are often not well understood. One particular focus of our group is on deep generative models which are important for understanding the principles o... Read More