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Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving.
Stereo processing by semiglobal matching and mutual information
Heiko Hirschmüller · 2007
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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The mapillary vistas dataset for semantic understanding of street scenes
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder · 2017
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Playing for benchmarks
Stephan R Richter, Zeeshan Hayder, and Vladlen Koltun · 2017
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The apolloscape dataset for autonomous driving
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang · 2018
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Synscapes: A photorealistic synthetic dataset for street scene parsing
Magnus Wrenninge and Jonas Unger · 2018
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Bdd100k: A diverse driving video database with scalable annotation tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell · 2018
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https://waymo.com/open/ , 2019
Waymo open dataset: An autonomous driving dataset · 2019
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Monofenet: Monocular 3d object detection with feature enhancement networks
Wentao Bao, Bin Xu, and Zhenzhong Chen · 2019
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Boxy vehicle detection in large images
Karsten Behrendt · 2019
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M3d-rpn: Monocular 3d region proposal network for object detection
Garrick Brazil and Xiaoming Liu · 2019
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Monocular 3d object detection leveraging accurate proposals and shape reconstruction
Jason Ku, Alex D Pon, and Steven L Waslander · 2019
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Gs3d: An efficient 3d object detection framework for autonomous driving
Buyu Li, Wanli Ouyang, Lu Sheng, Xingyu Zeng, and Xiaogang Wang · 2019
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Deep fitting degree scoring network for monocular 3d object detection
Lijie Liu, Jiwen Lu, Chunjing Xu, Qi Tian, and Jie Zhou · 2019
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Accurate monocular 3d object detection via color-embedded 3d reconstruction for autonomous driving
Xinzhu Ma, Zhihui Wang, Haojie Li, Pengbo Zhang, Wanli Ouyang, and Xin Fan · 2019
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Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape
Fabian Manhardt, Wadim Kehl, and Adrien Gaidon · 2019
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Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
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Argoverse: 3d tracking and forecasting with rich maps
Ming-Fang Chang, John W Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, and James Hays · 2019
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Visibility guided nms: Efficient boosting of amodal object detection in crowded traffic scenes
Nils Gählert, Niklas Hanselmann, Uwe Franke, and Joachim Denzler · 2019
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A2D2: AEV Autonomous Driving Dataset
Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S. Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian Mühlegg, Sebastian Dorn, Tiffany Fernandez, Martin Jänicke, Sudesh Mirashi, Chiragkumar Savani, Martin Sturm, Oleksandr Vorobiov, Martin Oelker, Sebastian Garreis, and Peter Schuberth · 2019
Cited alongside, same era.
Lyft level 5 av dataset 2019
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet · 2019
Cited alongside, same era.
Quang-Hieu Pham, Pierre Sevestre, Ramanpreet Singh Pahwa, Huijing Zhan, Chun Ho Pang, Yuda Chen, Armin Mustafa, Vijay Chandrasekhar, and Jie Lin · 2019
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Disentangling monocular 3d object detection
Andrea Simonelli, Samuel Rota Bulo, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder · 2019
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Std: Sparse-to-dense 3d object detector for point cloud
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
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Yohann Cabon, Naila Murray, and Martin Humenberger · 2020
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Sarpnet: Shape attention regional proposal network for lidar-based 3d object detection
Yangyang Ye, Houjin Chen, Chi Zhang, Xiaoli Hao, and Zhaoxiang Zhang · 2020
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