no code implementations • 16 Sep 2021 • Robert McCraith, Lukas Neumann, Andrea Vedaldi
Vision is one of the primary sensing modalities in autonomous driving.
1 code implementation • 16 Sep 2021 • Robert McCraith, Eldar Insafutdinov, Lukas Neumann, Andrea Vedaldi
We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects.
no code implementations • CVPR 2021 • Lukas Neumann, Andrea Vedaldi
Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e. g. breaking) to take place.
no code implementations • 16 Sep 2020 • Robert McCraith, Lukas Neumann, Andrea Vedaldi
In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal.
no code implementations • 13 Apr 2020 • Robert McCraith, Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
Recent advances in self-supervised learning havedemonstrated that it is possible to learn accurate monoculardepth reconstruction from raw video data, without using any 3Dground truth for supervision.
no code implementations • ICCV 2017 • Michal Busta, Lukas Neumann, Jiri Matas
A method for scene text localization and recognition is proposed.
4 code implementations • 26 Jan 2016 • Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, Serge Belongie
The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images.
no code implementations • ICCV 2015 • Michal Busta, Lukas Neumann, Jiri Matas
After a novel efficient classification step, the number of regions is reduced to 7 times less than the standard method and is still almost 3 times faster.