Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Peter Pinggera"'
Autor:
Stefan Andreas Baur, David Josef Emmerichs, Frank Moosmann, Peter Pinggera, Bjorn Ommer, Andreas Geiger
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9334911bb30631d45f39dfc2a8f63683
https://doi.org/10.1109/iccv48922.2021.01288
https://doi.org/10.1109/iccv48922.2021.01288
Publikováno v:
ICRA
The most successful methods for LiDAR-based 3D object detection use sequences of point clouds in order to exploit the increased data density through temporal aggregation. However, common aggregation methods are rarely able to capture fast-moving obje
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68b4776534699a0ffa4796d5de62ee07
https://doi.org/10.1109/icra48506.2021.9561644
https://doi.org/10.1109/icra48506.2021.9561644
Autor:
Markus Enzweiler, David Pfeiffer, Manuel Schäfer, Nick Schneider, J. Marius Zollner, Beate Schwarz, David Peter, Florian Piewak, Peter Pinggera
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030110239
ECCV Workshops (6)
ECCV Workshops (6)
Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27fb8767f70443c2af6ae1253af7f03d
https://doi.org/10.1007/978-3-030-11024-6_39
https://doi.org/10.1007/978-3-030-11024-6_39
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030129385
GCPR
GCPR
This paper presents a compact and accurate representation of 3D scenes that are observed by a LiDAR sensor and a monocular camera. The proposed method is based on the well-established Stixel model originally developed for stereo vision applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a7948ed554fc691a20392159a6349e2
https://doi.org/10.1007/978-3-030-12939-2_31
https://doi.org/10.1007/978-3-030-12939-2_31
Publikováno v:
ITSC
State-of-the-art approaches for the semantic labeling of LiDAR point clouds heavily rely on the use of deep Convolutional Neural Networks (CNNs). However, transferring network architectures across different LiDAR sensor types represents a significant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0008935391705d804b0de0df0c4f09c
Publikováno v:
2017 IEEE Intelligent Vehicles Symposium (IV)
Intelligent Vehicles Symposium
Intelligent Vehicles Symposium
The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To uti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81647c3fda74d31d6ec6615fb843684e
http://arxiv.org/abs/1612.06573
http://arxiv.org/abs/1612.06573
Autor:
Nick Schneider, Peter Pinggera, Uwe Franke, Lukas Schneider, Christoph Stiller, Marc Pollefeys
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319458854
GCPR
GCPR
We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8346dffd0d63719ce158239b4e7e860
https://doi.org/10.1007/978-3-319-45886-1_4
https://doi.org/10.1007/978-3-319-45886-1_4
Publikováno v:
IROS
Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1c5701bddc111784551f2e0be6a7852
https://doi.org/10.1109/iros.2015.7353537
https://doi.org/10.1109/iros.2015.7353537
Publikováno v:
Computer Vision – ECCV 2014 ISBN: 9783319106045
ECCV (2)
ECCV (2)
Modern applications of stereo vision, such as advanced driver assistance systems and autonomous vehicles, require highest precision when determining the location and velocity of potential obstacles. Subpixel disparity accuracy in selected image regio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cb108375d6424149c102719ef683802
https://doi.org/10.1007/978-3-319-10605-2_7
https://doi.org/10.1007/978-3-319-10605-2_7
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642406010
GCPR
GCPR
Precise stereo-based depth estimation at large distances is challenging: objects become very small, often exhibit low contrast in the image, and can hardly be separated from the background based on disparity due to measurement noise. In this paper we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2f8aaeeec64a40cc5c557ed815d07bb
https://doi.org/10.1007/978-3-642-40602-7_3
https://doi.org/10.1007/978-3-642-40602-7_3