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In this work, we propose an efficient and accurate monocular 3D detection framework in single shot.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Surf: Speeded up robust features
Herbert Bay, Tinne Tuytelaars, and Luc Van Gool · 2006
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G 2 o: A general framework for graph optimization
Rainer Kummerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, and Wolfram Burgard · 2011
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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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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Are cars just 3d boxes?-jointly estimating the 3d shape of multiple objects
Muhammad Zeeshan Zia, Michael Stark, and Konrad Schindler · 2014
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Data-driven 3d voxel patterns for object category recognition
Yu Xiang, Wongun Choi, Yuanqing Lin, and Silvio Savarese · 2015
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A unified multi-scale deep convolutional neural network for fast object detection
Zhaowei Cai, Quanfu Fan, Rogerio S Feris, and Nuno Vasconcelos · 2016
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Monocular 3d object detection for autonomous driving
Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Vehicle detection from 3d lidar using fully convolutional network
Bo Li, Tianlei Zhang, and Tian Xia · 2016
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Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios?
Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger · 2017
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Deep manta: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image
Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Céline Teulière, and Thierry Chateau · 2017
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3d object proposals using stereo imagery for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2017
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Multi-view 3d object detection network for autonomous driving
Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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3d bounding box estimation using deep learning and geometry
Voxelnet: End-to-end learning for point cloud based 3d object detection
Yin Zhou and Oncel Tuzel · 2018
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M3d-rpn: Monocular 3d region proposal network for object detection
Garrick Brazil and Xiaoming Liu · 2019
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Mono3d++: Monocular 3d vehicle detection with two-scale 3d hypotheses and task priors
Tong He and Stefano Soatto · 2019
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Foveabox: Beyond anchor-based object detector
Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, and Jianbo Shi · 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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Arsalan Mousavian, Dragomir Anguelov, John Flynn, and Jana Kosecka · 2017
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Reconstructing vehicles from a single image: Shape priors for road scene understanding
J Krishna Murthy, GV Sai Krishna, Falak Chhaya, and K Madhava Krishna · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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3d object localisation from multi-view image detections
Cosimo Rubino, Marco Crocco, and Alessio Del Bue · 2017
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Subcategory-aware convolutional neural networks for object proposals and detection
Yu Xiang, Wongun Choi, Yuanqing Lin, and Silvio Savarese · 2017
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Birdnet: a 3d object detection framework from lidar information
Jorge Beltrán, Carlos Guindel, Francisco Miguel Moreno, Daniel Cruzado, Fernando Garcia, and Arturo De La Escalera · 2018
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Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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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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Stereo r-cnn based 3d object detection for autonomous driving
Peiliang Li, Xiaozhi Chen, and Shaojie Shen · 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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Shift r-cnn: Deep monocular 3d object detection with closed-form geometric constraints
Andretti Naiden, Vlad Paunescu, Gyeongmo Kim, ByeongMoon Jeon, and Marius Leordeanu · 2019
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Monogrnet: A geometric reasoning network for monocular 3d object localization
Zengyi Qin, Jinglu Wang, and Yan Lu · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
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Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2019
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Cubeslam: Monocular 3-d object slam
Shichao Yang and Sebastian Scherer · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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