Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Wouter Van Gansbeke"'
Autor:
Marc Proesmans, Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Dengxin Dai, Luc Van Gool
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 44(7)
With the advent of deep learning, many dense prediction tasks, i.e. tasks that produce pixel-level predictions, have seen significant performance improvements. The typical approach is to learn these tasks in isolation, that is, a separate neural netw
Publikováno v:
IEEE Robotics and Automation Letters, 5 (4)
Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video input. Our method produces a time series of depth maps, which makes it a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::822e751132ac6a04e210a05ff3ef0cdb
http://arxiv.org/abs/2001.02613
http://arxiv.org/abs/2001.02613
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030586065
ECCV (10)
ECCV (10)
Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2e6bbd37f0c4ca4a9a57064f6875512
Publikováno v:
MVA
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on
Publikováno v:
ICCV Workshops
Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline) is fitted to the post-processed mask next. The problem with such a tw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9aa0af590b9bd37227191e47d777905
http://arxiv.org/abs/1902.00293
http://arxiv.org/abs/1902.00293