Learning Depth from Single Monocular Images Using Deep Convolutional Neural …

Articles citing Learning Depth from Single Monocular Images Using Deep Convolutional Neural …

Title / Author	Year
Fully convolutional networks for semantic segmentation J Long, E Shelhamer, T Darrell Proceedings of the IEEE conference on computer vision and pattern …	2015
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation G Lin, A Milan, C Shen, I Reid Proceedings of the IEEE conference on computer vision and pattern …	2017
Unsupervised monocular depth estimation with left-right consistency C Godard, O Mac Aodha, GJ Brostow Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Unsupervised learning of depth and ego-motion from video T Zhou, M Brown, N Snavely, DG Lowe Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Efficient piecewise training of deep structured models for semantic segmentation G Lin, C Shen, A Van Den Hengel, I Reid Proceedings of the IEEE conference on computer vision and pattern …	2016
Unsupervised cnn for single view depth estimation: Geometry to the rescue R Garg, VK Bg, G Carneiro, I Reid European conference on computer vision, 740-756	2016
End-to-end learning of geometry and context for deep stereo regression A Kendall, H Martirosyan, S Dasgupta, P Henry, R Kennedy, A Bachrach, ... Proceedings of the IEEE International Conference on Computer Vision, 66-75	2017
Demon: Depth and motion network for learning monocular stereo B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Cad2rl: Real single-image flight without a single real image F Sadeghi, S Levine arXiv preprint arXiv:1611.04201	2016
Deep ordinal regression network for monocular depth estimation H Fu, M Gong, C Wang, K Batmanghelich, D Tao Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Aod-net: All-in-one dehazing network B Li, X Peng, Z Wang, J Xu, D Feng Proceedings of the IEEE international conference on computer vision, 4770-4778	2017
Geonet: Unsupervised learning of dense depth, optical flow and camera pose Z Yin, J Shi Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory I Kokkinos Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints R Mahjourian, M Wicke, A Angelova Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction H Zhan, R Garg, C Saroj Weerasekera, K Li, H Agarwal, I Reid Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Sparsity invariant cnns J Uhrig, N Schneider, L Schneider, U Franke, T Brox, A Geiger 2017 international conference on 3D Vision (3DV), 11-20	2017
Learning depth from monocular videos using direct methods C Wang, J Miguel Buenaposada, R Zhu, S Lucey Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
Fcos: Fully convolutional one-stage object detection Z Tian, C Shen, H Chen, T He Proceedings of the IEEE international conference on computer vision, 9627-9636	2019
Learning to count with cnn boosting E Walach, L Wolf European conference on computer vision, 660-676	2016
Digging into self-supervised monocular depth estimation C Godard, O Mac Aodha, M Firman, GJ Brostow Proceedings of the IEEE international conference on computer vision, 3828-3838	2019

1 - 20

Dates and citation counts are estimated and are determined automatically by a computer program.