Autonomous Vision – Max Planck Institute for Intelligent Systems

Autonomous Vision Event 28-09-2020 - 01-10-2020 German Conference on Pattern Recognition DAGM-GCPR 2020 in Tübingen The 42nd German Conference on Pattern Recognition (DAGM-GCPR 2020), the 25th International Symposium on Vision, Modeling and Visualization (VMV 2020) and the 10th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM 2020) will for the first time be co-located in Tübingen this year! Andreas Geiger

Autonomous Vision Event 30-06-2019 - 07-07-2019 3rd Computational Vision Summer School The 3rd Computational Vision Summer School offers a broad perspective on biological vision and computer vision with a thorough understanding of the theoretical and computational challenges involved. The school is unique in bringing together people from diverse disciplines who all share a computational view of vision. The faculty consists of renowned senior researchers in the field, teaching lectures and providing hands-on tutorials on topics ranging from early vision to image understanding. Michael Black Andreas Geiger Siyu Tang

Autonomous Vision Talk Zeynep Akata 06-07-2018 Representing and Explaining Novel Concepts With Minimal Supervision Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image aspects which justify visual predictions. In this talk, I will present my past and current work on Zero-Shot Learning, Vision and Language for Generative Modeling and Explainable Artificial Intelligence in that (1) how we can generalize the image... Andreas Geiger

Autonomous Vision Talk Vagia Tsiminaki 15-12-2017 Appearance Modeling for 4D Multi-view Representations The emergence of multi-view capture systems has yield a tremendous amount of video sequences. The task of capturing spatio-temporal models from real world imagery (4D modeling) should arguably benefit from this enormous visual information. In order to achieve highly realistic representations both geometry and appearance need to be modeled in high precision. Yet, even with the great progress of the geometric modeling, the appearance aspect has not been fully explored and visual quality can still be improved. I will explain how we can optimally exploit the redundant visual information of ... Despoina Paschalidou

Autonomous Vision Talk Christoph Mayer 17-11-2017 Operator splitting: a versatile framework for variational image processing tasks. Variational image processing translates image processing tasks into optimisation problems. The practical success of this approach depends on the type of optimisation problem and on the properties of the ensuing algorithm. A recent breakthrough was to realise that old first-order optimisation algorithms based on operator splitting are particularly suited for modern data analysis problems. Operator splitting techniques decouple complex optimisation problems into many smaller and simpler sub-problems. In this talk I will revise the variational segmentation problem and a common family of al... Benjamin Coors

Autonomous Vision Talk Michiel Vlaminck 20-10-2017 3D lidar mapping: an accurate and performant approach In my talk I will present my work regarding 3D mapping using lidar scanners. I will give an overview of the SLAM problem and its main challenges: robustness, accuracy and processing speed. Regarding robustness and accuracy, we investigate a better point cloud representation based on resampling and surface reconstruction. Moreover, we demonstrate how it can be incorporated in an ICP-based scan matching technique. Finally, we elaborate on globally consistent mapping using loop closures. Regarding processing speed, we propose the integration of our scan matching in a multi-resolution scheme an... Simon Donne

Autonomous Vision Talk Slobodan Ilic and Mira Slavcheva 19-10-2017 SDF-2-SDF: 3D Reconstruction of Rigid and Deformable Objects from RGB-D Videos In this talk we will address the problem of 3D reconstruction of rigid and deformable objects from a single depth video stream. Traditional 3D registration techniques, such as ICP and its variants, are wide-spread and effective, but sensitive to initialization and noise due to the underlying correspondence estimation procedure. Therefore, we have developed SDF-2-SDF, a dense, correspondence-free method which aligns a pair of implicit representations of scene geometry, e.g. signed distance fields, by minimizing their direct voxel-wise difference. In its rigid variant, we apply it for static ... Fatma Güney

Autonomous Vision Event 07-09-2017 - 08-09-2017 AVG Summer Retreat

Autonomous Vision Talk Matteo Poggi 12-07-2017 Deep Learning for stereo matching and related tasks Recently, deep learning proved to be successful also on low level vision tasks such as stereo matching. Another recent trend in this latter field is represented by confidence measures, with increasing effectiveness when coupled with random forest classifiers or CNNs. Despite their excellent accuracy in outliers detection, few other applications rely on them. In the first part of the talk, we'll take a look at the latest proposal in terms of confidence measures for stereo matching, as well as at some novel methodologies exploiting these very accurate cues. In the second part, we'll talk ab... Yiyi Liao

Autonomous Vision Talk Matthias Niessner 14-06-2017 Reconstructing and Understanding 3D Indoor Environments In the recent years, commodity 3D sensors have become easily and widely available. These advances in sensing technology have spawned significant interest in using captured 3D data for mapping and semantic understanding of 3D environments. In this talk, I will give an overview of our latest research in the context of 3D reconstruction of indoor environments. I will further talk about the use of 3D data in the context of modern machine learning techniques. Specifically, I will highlight the importance of training data, and how can we efficiently obtain labeled and self-supervised ground truth... Despoina Paschalidou

Autonomous Vision Talk Alexey Dosovitskiy 06-06-2017 Learning to see and act in dynamic three-dimensional environments Our world is dynamic and three-dimensional. Understanding the 3D layout of scenes and the motion of objects is crucial for successfully operating in such an environment. I will talk about two lines of recent research in this direction. One is on end-to-end learning of motion and 3D structure: optical flow estimation, binocular and monocular stereo, direct generation of large volumes with convolutional networks. The other is on sensorimotor control in immersive three-dimensional environments, learned from experience or from demonstration. Lars Mescheder Aseem Behl

Autonomous Vision Event 01-06-2017 - 02-06-2017 AVG Spring Retreat

Autonomous Vision Talk Carolin Schmitt 11-05-2017 Biquadratic Forms and Semi-Definite Relaxations I'll present my master thesis "Biquadratic Forms and Semi-Definite Relaxations". It is about biquadratic optimization programs (which are NP-hard generally) and examines a condition under which there exists an algorithm that finds a solution to every instance of the problem in polynomial time. I'll present a counterexample for which this is not possible generally and face the question of what happens if further knowledge about the variables over which we optimise is applied. Fatma Güney

Autonomous Vision Event 06-02-2017 - 09-02-2017 Computer Vision Retreat Andreas Geiger

Autonomous Vision Talk Laura Leal-Taixé 08-12-2016 Deep Learning and its Relationship with Time In this talk I am going to present the work we have been doing at the Computer Vision Lab of the Technical University of Munich which started as an attempt to better deal with videos (and therefore the time domain) within neural network architectures. Joel Janai

Autonomous Vision Talk Carsten Rother 21-11-2016 A Collection of Recent Work: From 6D Pose estimation via MRF-Diversity to zebrafish detection In this talk I will present the portfolio of work we conduct in our lab. Herby, I will present three recent body of work in more detail. This is firstly our work on learning 6D Object Pose estimation and Camera localizing from RGB or RGBD images. I will show that by utilizing the concepts of uncertainty and learning to score hypothesis, we can improve the state of the art. Secondly, I will present a new approach for inferring multiple diverse labeling in a graphical model. Besides guarantees of an exact solution, our method is also faster than existing techniques. Finally, I will present a ... Aseem Behl

Autonomous Vision Talk Bogdan Savchynskyy 21-11-2016 Future of graphical models: more modeling power, parallelization, scalable solvers We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future. Aseem Behl

Autonomous Vision Talk Anton Milan 22-07-2016 Bipartite Matching and Multi-target Tracking Matching between two sets arises in various areas in computer vision, such as feature point matching for 3D reconstruction, person re-identification for surveillance or data association for multi-target tracking. Most previous work focused either on designing suitable features and matching cost functions, or on developing faster and more accurate solvers for quadratic or higher-order problems. In the first part of my talk, I will present a strategy for improving state-of-the-art solutions by efficiently computing the marginals of the joint matching probability. The second part of my talk wi...