Fetching the paper…
Reading the bibliography…
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling.
Scalable parallel programming with cuda: Is cuda the parallel programming model that application developers have been waiting for?
John Nickolls, Ian Buck, Michael Garland, and Kevin Skadron · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Learning depth from single monocular images using deep convolutional neural fields
Fayao Liu, Chunhua Shen, Guosheng Lin, and Ian Reid · 2015
Earlier work this paper cites.
Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P. Lichtenberg, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, B. G. Vijay Kumar, Gustavo Carneiro, and Ian Reid · 2016
Earlier work this paper cites.
Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2016
Earlier work this paper cites.
Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
A multi-view stereo benchmark with high-resolution images and multi-camera videos
Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger · 2017
Earlier work this paper cites.
Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
Earlier work this paper cites.
Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William B Shen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2018
Earlier work this paper cites.
Cam-convs: Camera-aware multi-scale convolutions for single-view depth
Jose M Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, and Javier Civera · 2019
Earlier work this paper cites.
From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
DIODE: A dense indoor and outdoor depth dataset
Igor Vasiljevic, Nicholas I. Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, and Gregory Shakhnarovich · 2019
Cited alongside, same era.
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
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Is pseudo-lidar needed for monocular 3d object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon · 2021
Later among the works it cites.
Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, and Quoc V. Le · 2019
Cited alongside, same era.
Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios
Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou · 2019
Cited alongside, same era.
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, and Vladlen Koltun · 2019
Cited alongside, same era.
Mapillary planet-scale depth dataset
Manuel Lopez Antequera, Pau Gargallo, Markus Hofinger, Samuel Rota Bulò, Yubin Kuang, and Peter Kontschieder · 2020
Cited alongside, same era.
Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2020
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
Cited alongside, same era.
A2D2: Audi 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 · 2020
Cited alongside, same era.
3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
Cited alongside, same era.
Argoverse 2: Next generation datasets for self-driving perception and forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays · 2021
Later among the works it cites.
Transformer-based attention networks for continuous pixel-wise prediction
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, and Elisa Ricci · 2021
Later among the works it cites.
Learning to recover 3d scene shape from a single image
Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen · 2021
Later among the works it cites.
Depth-supervised nerf: Fewer views and faster training for free
Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan · 2022
Later among the works it cites.
Towards real-time monocular depth estimation for robotics: A survey
Xingshuai Dong, Matthew A Garratt, Sreenatha G Anavatti, and Hussein A Abbass · 2022
Later among the works it cites.
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, et al · 2022
Later among the works it cites.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
Later among the works it cites.
From 2d to 3d: Re-thinking benchmarking of monocular depth prediction
Evin Pınar Örnek, Shristi Mudgal, Johanna Wald, Yida Wang, Nassir Navab, and Federico Tombari · 2022
Later among the works it cites.
P3Depth: Monocular depth estimation with a piecewise planarity prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger, and Luc Van Gool · 2022
Later among the works it cites.
Neural window fully-connected crfs for monocular depth estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan · 2022
Later among the works it cites.
Zoedepth: Zero-shot transfer by combining relative and metric depth
Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller · 2023
Later among the works it cites.
Towards zero-shot scale-aware monocular depth estimation
Vitor Guizilini, Igor Vasiljevic, Dian Chen, Rareș Ambruș, and Adrien Gaidon · 2023
Later among the works it cites.
iDisc: Internal discretization for monocular depth estimation
Luigi Piccinelli, Christos Sakaridis, and Fisher Yu · 2023
Later among the works it cites.
Metric3d: Towards zero-shot metric 3d prediction from a single image
Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, and Chunhua Shen · 2023
Later among the works it cites.