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Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance.
Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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3d object proposals for accurate object class detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G Berneshawi, Huimin Ma, Sanja Fidler, and Raquel Urtasun · 2015
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, V. Kumar, G. Carneiro, and I. Reid · 2016
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Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. Reid · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Y. Cao, Z. Wu, and C. Shen · 2017
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Unsupervised monocular depth estimation with left-right consistency
C. Godard, O.M. Aodha, and G.J. Brostow · 2017
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Semi-supervised deep learning for monocular depth map prediction
Y. Kuznietsov, J. Stückler, and B. Leibe · 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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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Faster R-CNN: Towards real time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. Lowe · 2017
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Monocular depth estimation by learning from heterogeneous datasets
Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui, Fahd Bouzaraa, and Antonio M. López · 2018
Cited alongside, same era.
AdaDepth: Unsupervised content congruent adaptation for depth estimation
Jogendra Nath Kundu, Phani Krishna Uppala, Anuj Pahuja, and R. Venkatesh Babu · 2018
Cited alongside, same era.
Structured attention guided convolutional neural fields for monocular depth estimation
Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, and Elisa Ricci · 2018
Cited alongside, same era.
GeoNet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 2018
Cited alongside, same era.
T2Net: Synthetic-to-realistic translation for solving single-image depth estimation tasks
Chuanxia Zheng, Tat-Jen Cham, and Jianfei Cai · 2018
Cited alongside, same era.
VoxelNet: End-to-end learning for point cloud based 3D object detection
Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger · 2019
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Geometry-aware symmetric domain adaptation for monocular depth estimation
Shanshan Zhao, Huan Fu, Mingming Gong, and Dacheng Tao · 2019
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Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
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3D packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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SharinGAN: Combining synthetic and real data for unsupervised geometry estimation
Koutilya PNVR, Hao Zhou, and David Jacobs · 2020
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Yin Zhou and Oncel Tuzel · 2018
Cited alongside, same era.
Towards scene understanding: Unsupervised monocular depth estimation with semantic-aware representation
Po-Yi Chen, Alexander H. Liu, Yen-Cheng Liu, and Yu-Chiang Frank Wang · 2019
Cited alongside, same era.
Digging into self-supervised monocular depth estimation
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2019
Cited alongside, same era.
Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
Cited alongside, same era.
Gs3d: An efficient 3d object detection framework for autonomous driving
Buyu Li, Wanli Ouyang, Lu Sheng, Xingyu Zeng, and Xiaogang Wang · 2019
Cited alongside, same era.
Review of camera calibration algorithms
Li Long and Shan Dongri · 2019
Cited alongside, same era.
PointRCNN: 3D object proposal generation and detection from point cloud
Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
3DSSD: Point-based 3d single stage object detector
Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia · 2020
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Towards better generalization: Joint depth-pose learning without PoseNet
Wang Zhao, Shaohui Liu, Yezhi Shu, and Yong-Jin Liu · 2020
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Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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On deep learning techniques to boost monocular depth estimation for autonomous navigation
Raul de Queiroz Mendes, Eduardo Godinho Ribeiro, Nicolas dos Santos Rosa, and Valdir Grassi Jr · 2021
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Voxel R-CNN: Towards high performance voxel-based 3d object detection
Jiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou, Yanyong Zhang, and Houqiang Li · 2021
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Monocular depth estimation through virtual-world supervision and real-world sfm self-supervision
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen, Onay Urfalioglu, and Antonio M. López · 2021
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Center-based 3D object detection and tracking
Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl · 2021
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Monocular depth estimation through virtual-world supervision and real-world SfM self-supervision, 2022
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen, Onay Urfalioglu, and Antonio M. López · 2022
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