Challenge is held on Kaggle
Apr. 8 2019Jun. 3 2019
Held with CVPR’19
Jun. 16 2019, 8:30 - 12:30, PDTNoah Snavely (Cornell Tech / Google AI)
End-to-End Geometric Learning
Krystian Mikolajczyk (Imperial College London)
Recent Progress and Remaining Challenges in Local Features
Associate Professor at Cornell Tech
One often hears that vision systems should be trained end-to-end using deep learning. This talk will explore this idea in the context of 3D geometry, presenting end-to-end methods for a number of tasks, including keypoint detection, pose estimation, and view synthesis. I will show how the use of geometric reasoning as an end goal of learning can enable emergent discovery of good keypoints, systems for predicting 3D shape from single images, and more, all without the use of explicit supervision. I will relate these ideas back to the landmark recognition problem.
Associate Professor at Imperial College London
In many computer vision applications local image features and descriptors have been replaced by end-to-end learning based methods but still remain the preferred choice for estimating accurate 3D models in multiple view geometry, camera and object pose estimation, or efficient SLAM. Large benchmarks and synthetically generated training data stimulated the progress in nearly all areas of computer vision including keypoint detection and description. I will present some recent works in this domain including new multi-task benchmarks for feature matching and localization, new methods for keypoint extraction and description, as well as for improving their efficiency.
Associate Professor, Seoul National University (Primary Contact)
Software Engineer, Google (Primary Contact)
Software Engineer, Google
Professor, Columbia University
Associate Professor, Czech Technical University
Associate Professor, Chalmers University of Technology
Software Engineer, Google
Postdoc, Czech Technical University
Software Engineer, Google
Postdoc, Columbia University
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