EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching
暂无分享,去创建一个
Xu Zhao | Xiao Song | Hanwen Hu | Liangji Fang | Xu Zhao | Liangji Fang | Xiao Song | Hanwen Hu
[1] Yann LeCun,et al. Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches , 2015, J. Mach. Learn. Res..
[2] John Flynn,et al. Deep Stereo: Learning to Predict New Views from the World's Imagery , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Xiang Bai,et al. Richer Convolutional Features for Edge Detection , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Liang Wang,et al. A Deep Visual Correspondence Embedding Model for Stereo Matching Costs , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[6] Thomas Brox,et al. FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] Sanja Fidler,et al. The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Michael Suppa,et al. Stereo vision based indoor/outdoor navigation for flying robots , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[9] Thomas Brox,et al. A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Pietro Perona,et al. Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Qiong Yan,et al. Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[12] Yong-Sheng Chen,et al. Pyramid Stereo Matching Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Marc Pollefeys,et al. Patch Based Confidence Prediction for Dense Disparity Map , 2016, BMVC.
[14] John F. Canny,et al. A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Li Zhang,et al. Estimating Optimal Parameters for MRF Stereo from a Single Image Pair , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Nikos Komodakis,et al. Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Raquel Urtasun,et al. Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation , 2014, ECCV.
[19] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[20] Alex Kendall,et al. End-to-End Learning of Geometry and Context for Deep Stereo Regression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[21] Wei Liu,et al. ParseNet: Looking Wider to See Better , 2015, ArXiv.
[22] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[23] Oisin Mac Aodha,et al. Unsupervised Monocular Depth Estimation with Left-Right Consistency , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Yu Liu,et al. Learning Relaxed Deep Supervision for Better Edge Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Yucheng Wang,et al. Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture , 2018, AAAI.
[27] Hongdong Li,et al. Self-Supervised Learning for Stereo Matching with Self-Improving Ability , 2017, ArXiv.
[28] Zhidong Deng,et al. SegStereo: Exploiting Semantic Information for Disparity Estimation , 2018, ECCV.
[29] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Yuning Jiang,et al. What Can Help Pedestrian Detection? , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Andreas Geiger,et al. Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[34] Marc Pollefeys,et al. SGM-Nets: Semi-Global Matching with Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Ming-Hsuan Yang,et al. SegFlow: Joint Learning for Video Object Segmentation and Optical Flow , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[36] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Xi Wang,et al. High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth , 2014, GCPR.
[38] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[39] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[41] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] D. Scharstein,et al. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).
[43] Raquel Urtasun,et al. Efficient Deep Learning for Stereo Matching , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] G. Gerhart,et al. Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments , 2009 .
[45] H. Hirschmüller. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.
[46] Wei Chen,et al. Learning Deep Correspondence through Prior and Posterior Feature Constancy , 2017, ArXiv.
[47] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Pushmeet Kohli,et al. Object stereo — Joint stereo matching and object segmentation , 2011, CVPR 2011.
[49] Andreas Geiger,et al. Displets: Resolving stereo ambiguities using object knowledge , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Lior Wolf,et al. Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).