Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age, IEEE Transactions on Robotics, vol.32, issue.6, pp.1309-1332, 2016. ,
DOI : 10.1109/TRO.2016.2624754
VESTIBULO-OCULAR REFLEX ARC, Archives of Neurology And Psychiatry, vol.30, issue.2, pp.245-291, 1933. ,
DOI : 10.1001/archneurpsyc.1933.02240140009001
FlowNet: Learning Optical Flow with Convolutional Networks, 2015 IEEE International Conference on Computer Vision (ICCV), 2015. ,
DOI : 10.1109/ICCV.2015.316
URL : http://arxiv.org/abs/1504.06852
Depth map prediction from a single image using a multi-scale deep network, Advances in neural information processing systems, pp.2366-2374, 2014. ,
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue, 1603. ,
DOI : 10.1109/ICCV.2015.179
Are we ready for autonomous driving? The KITTI vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3354-3361, 2012. ,
DOI : 10.1109/CVPR.2012.6248074
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.228.9969
Learning long-range vision for autonomous off-road driving, Journal of Field Robotics, vol.23, issue.9, pp.120-144, 2009. ,
DOI : 10.1002/rob.20276
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.6891
Flownet 2.0: Evolution of optical flow estimation with deep networks. arXiv preprint, 2016. ,
Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint, 2015. ,
End-to-End Learning of Geometry and Context for Deep Stereo Regression, 2017. ,
Parallel Tracking and Mapping for Small AR Workspaces, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp.225-234, 2007. ,
DOI : 10.1109/ISMAR.2007.4538852
Unsupervised learning of depth and motion. CoRR, abs/1312, 2013. ,
ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
DOI : 10.1162/neco.2009.10-08-881
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.1115
Efficient Deep Learning for Stereo Matching, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5695-5703, 2016. ,
DOI : 10.1109/CVPR.2016.614
Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967. ,
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras, IEEE Transactions on Robotics, 2016. ,
DOI : 10.1109/TRO.2017.2705103
END-TO-END DEPTH FROM MOTION WITH STABILIZED MONOCULAR VIDEOS, submitted to the International Conference on Unmanned Aerial Vehicles in Geomatics, p.2017 ,
DOI : 10.5194/isprs-annals-IV-2-W3-67-2017
URL : https://hal.archives-ouvertes.fr/hal-01587652
Indoor Segmentation and Support Inference from RGBD Images, ECCV, 2012. ,
DOI : 10.1007/978-3-642-33715-4_54
SfM-Net: Learning of Structure and Motion from Video. ArXiv e-prints, 2017. ,
Empirical evaluation of rectified activations in convolutional network, 1505. ,
Computing the stereo matching cost with a convolutional neural network, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1592-1599, 2015. ,
DOI : 10.1109/CVPR.2015.7298767
Unsupervised learning of depth and ego-motion from video, CVPR, 2017. ,