I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing and reconstruction. During my Ph.D. I have developed probabilistic models for 3D traffic scene understanding from movable platforms.

I am interested in computer vision, machine learning, robotics and human-autonomy interaction. I aim to enable intelligent systems (e.g., autonomous vehicles) that can automatically understand and interactively learn from their environment. Towards this goal, I work to address real-world challenges in robust perception and action of safety-critical autonomous systems.

Advisor: Andreas Geiger

My research is in the area of Computer Vision and Machine Learning. Currently, I am working on problems related to estimation of 3D motion fields from stereo image sequences.

My research is at the intersection of robotics, machine learning and computer vision. I am currently interested in scene representations for improving the robustness and generalization of learned vision-based control policies.

My research interests lie at the intersection of computer vision and deep learning. In particular, I am interested in exploring how to move away from full supervision by leveraging transfer learning and generative models. I am also part of the Image Understanding Group at Mercedes-Benz AG.

Advisor: Andreas Geiger

My research interests include computer vision and deep learning, in especially their applications for semantic and geometric scene understanding in 3D.

My research focuses on continuous representations and generative models.

I am a second-year PhD student supervised by Prof. Andreas Geiger and I am also part of the machine learning team of the ETAS GmbH, Bosch Group. My research focuses on 3D reconstruction and generative models for 3D objects. I am particularly interested in investigating novel object representation that are feasible for learning-based methods.

One of the most challenging tasks of Computer Vision is to endow computers with the ability to discover the underlying relationships between the objects in a scene. The large amount of available labeled data as well as the fast progress in deep learning has significantly advanced many Computer Vision tasks, such as object segmentation. optical flow estimation, action recognition etc. However, a truly intelligent system would ideally be able to infer high-level semantics underlying human actions such as motivation, intent and emotion. However, all human actions involve some uncertainty. To this end, I would like to either develop or to further enhance existing methodologies that incorporate such uncertainties. For now, I have worked on the 3D reconstruction task, by developing a model able to incorporate uncertainties in the image formation process.

My research is at the intersection of machine learning, computer vision and robotics. Specifically, I am interested in developing robust driving policies for autonomous navigation in dense urban environments.

I am studying how self-supervised visual representation learning and meta-learning can be brought together in a principled way.

My research focuses on the unsupervised learning of an interpretable and causal state representation. This representation can be used, for example, for scalable end-to-end autonomous driving.

My research interest lies in the intersection of Computer Vision, Computer Graphics and Machine Learning. Currently I am working on reflectance and material estimation from RGB video input.

My research is at the intersection of Machine Learning, Deep Learning and Computer Vision. Currently, I am interested in 3D geometric scene understanding.

I'm a third year PhD student at the University of Bologna and currently a visiting PhD student at the Max Planck Institute for Intelligent System and University of Tübingen (Autonomous Vision Group). My research interests include Deep Learning and Computer Vision, in particular stereo matching and monocular depth estimation related tasks.