Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN
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[1] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Matthias Nießner,et al. Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..
[3] Feng Liu,et al. Depth Enhancement via Low-Rank Matrix Completion , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Andrew W. Fitzgibbon,et al. KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.
[5] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[6] Yinda Zhang,et al. Deep Depth Completion of a Single RGB-D Image , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Dani Lischinski,et al. Joint bilateral upsampling , 2007, SIGGRAPH 2007.
[8] Tae Hyun Kim,et al. Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Chongyu Chen,et al. Learning Dynamic Guidance for Depth Image Enhancement , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Xiaoou Tang,et al. Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.
[11] Stephen Lin,et al. Shading-Based Shape Refinement of RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[12] In-So Kweon,et al. High Quality Shape from a Single RGB-D Image under Uncalibrated Natural Illumination , 2013, 2013 IEEE International Conference on Computer Vision.
[13] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[14] Matthias Nießner,et al. BundleFusion , 2016, TOGS.
[15] Ivana Tosic,et al. Learning Joint Intensity-Depth Sparse Representations , 2012, IEEE Transactions on Image Processing.
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[18] Sanja Fidler,et al. Holistic Scene Understanding for 3D Object Detection with RGBD Cameras , 2013, 2013 IEEE International Conference on Computer Vision.
[19] Hui Lin,et al. Depth image enhancement for Kinect using region growing and bilateral filter , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[20] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[22] Hiroshi Ishikawa,et al. Globally and locally consistent image completion , 2017, ACM Trans. Graph..
[23] Renjie Liao,et al. Deep Edge-Aware Filters , 2015, ICML.
[24] Wolfram Burgard,et al. Multimodal deep learning for robust RGB-D object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[25] David A. Forsyth,et al. Sparse depth super resolution , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Xiaoou Tang,et al. Depth Map Super-Resolution by Deep Multi-Scale Guidance , 2016, ECCV.
[27] Roberto Manduchi,et al. Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[28] Michael Schmeing,et al. Edge-aware depth image filtering using color segmentation , 2014, Pattern Recognit. Lett..
[29] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[31] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[32] Jianxiong Xiao,et al. SUN RGB-D: A RGB-D scene understanding benchmark suite , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[34] Narendra Ahuja,et al. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Martin Kleinsteuber,et al. A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Martin A. Riedmiller,et al. A learned feature descriptor for object recognition in RGB-D data , 2012, 2012 IEEE International Conference on Robotics and Automation.
[37] J. Weickert,et al. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .
[38] Qi Zhang,et al. Rolling Guidance Filter , 2014, ECCV.
[39] Ce Liu,et al. Deep Convolutional Neural Network for Image Deconvolution , 2014, NIPS.
[40] Jitendra Malik,et al. Learning Rich Features from RGB-D Images for Object Detection and Segmentation , 2014, ECCV.
[41] Juan Song,et al. Depth enhancement with improved exemplar-based inpainting and joint trilateral guided filtering , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[42] Shahram Izadi,et al. Real-time shading-based refinement for consumer depth cameras , 2014, ACM Trans. Graph..
[43] Stephen Lin,et al. Data-driven depth map refinement via multi-scale sparse representation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Seung-Won Jung,et al. Directional Joint Bilateral Filter for Depth Images , 2014, Sensors.
[45] Li Xu,et al. Mutual-Structure for Joint Filtering , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[46] Eirikur Agustsson,et al. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).