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Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image.
Learning depth from single monocular images
Ashutosh Saxena, Sung H Chung, Andrew Y Ng, et al · 2005
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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Learning monocular depth by distilling cross-domain stereo networks
Xiaoyang Guo, Hongsheng Li, Shuai Yi, Jimmy Ren, and Xiaogang Wang · 2018
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Activation functions: Comparison of trends in practice and research for deep learning
Chigozie Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall · 2018
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Geonet: Geometric neural network for joint depth and surface normal estimation
Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, and Jiaya Jia · 2018
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Soft labels for ordinal regression
Raul Diaz and Amit Marathe · 2019
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Learning internal dense but external sparse structures of deep convolutional neural network
Yiqun Duan and Chen Feng · 2019
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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
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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
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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, et al · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
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Pattern-affinitive propagation across depth, surface normal and semantic segmentation
Zhenyu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, and Jian Yang · 2019
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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, et al · 2020
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Diffusion models for implicit image segmentation ensembles
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin · 2021
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Channel-wise attention-based network for self-supervised monocular depth estimation
Jiaxing Yan, Hong Zhao, Penghui Bu, and YuSheng Jin · 2021
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Transformer-based attention networks for continuous pixel-wise prediction
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, and Elisa Ricci · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zi-Hang Jiang, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2022
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
Cited alongside, same era.
Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
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Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
Adrian Johnston and Gustavo Carneiro · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Bidirectional attention network for monocular depth estimation
Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam, and Mannat Kaur Bingbing Liu · 2021
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Denoising pretraining for semantic segmentation
Emmanuel Asiedu Brempong, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, and Mohammad Norouzi · 2022
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Diffusiondet: Diffusion model for object detection
Shoufa Chen, Peize Sun, Yibing Song, and Ping Luo · 2022
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A generalist framework for panoptic segmentation of images and videos
Ting Chen, Lala Li, Saurabh Saxena, Geoffrey Hinton, and David J Fleet · 2022
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Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clement Vignac, and Max Welling · 2022
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Diffusion adversarial representation learning for self-supervised vessel segmentation
Boah Kim, Yujin Oh, and Jong Chul Ye · 2022
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Zhenyu Li, Zehui Chen, Xianming Liu, and Junjun Jiang · 2022
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Binsformer: Revisiting adaptive bins for monocular depth estimation
Zhenyu Li, Xuyang Wang, Xianming Liu, and Junjun Jiang · 2022
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P3depth: Monocular depth estimation with a piecewise planarity prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger, and Luc Van Gool · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2022
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Neural window fully-connected crfs for monocular depth estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan · 2022
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New crfs: Neural window fully-connected crfs for monocular depth estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan · 2022
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Attention attention everywhere: Monocular depth prediction with skip attention
Ashutosh Agarwal and Chetan Arora · 2023
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Va-depthnet: A variational approach to single image depth prediction
Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, and Luc Van Gool · 2023
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Urcdc-depth: Uncertainty rectified cross-distillation with cutflip for monocular depth estimation
Shuwei Shao, Zhongcai Pei, Weihai Chen, Ran Li, Zhong Liu, and Zhengguo Li · 2023
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