Recurrent Slice Networks for 3D Segmentation of Point Clouds
暂无分享,去创建一个
[1] Jing Huang,et al. Pole-like object detection and classification from urban point clouds , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[2] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[3] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[4] Ulrich Neumann,et al. Exemplar-Based 3D Shape Segmentation in Point Clouds , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[5] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[6] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[7] Zhichao Zhou,et al. DeepPano: Deep Panoramic Representation for 3-D Shape Recognition , 2015, IEEE Signal Processing Letters.
[8] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Leonidas J. Guibas,et al. SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Ulrich Neumann,et al. Pipe-Run Extraction and Reconstruction from Point Clouds , 2014, ECCV.
[11] Suya You,et al. Estimation of camera pose with respect to terrestrial LiDAR data , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).
[12] Leonidas J. Guibas,et al. Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Silvio Savarese,et al. SEGCloud: Semantic Segmentation of 3D Point Clouds , 2017, 2017 International Conference on 3D Vision (3DV).
[14] Silvio Savarese,et al. 3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Victor S. Lempitsky,et al. Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[17] Ulrich Neumann,et al. Training-Based Object Recognition in Cluttered 3D Point Clouds , 2013, 2013 International Conference on 3D Vision.
[18] Benjamin Graham,et al. Spatially-sparse convolutional neural networks , 2014, ArXiv.
[19] Wei Wu,et al. Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 , 2017, ArXiv.
[20] Jing Huang,et al. Point cloud labeling using 3D Convolutional Neural Network , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[21] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[23] Jing Huang,et al. Automatic 3D industrial point cloud modeling and recognition , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).
[24] Rob Fergus,et al. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] Chao Yang,et al. Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[27] Ulrich Neumann,et al. IPDC: Iterative part-based dense correspondence between point clouds , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[28] Jing Huang,et al. Detecting Objects in Scene Point Cloud: A Combinational Approach , 2013, 2013 International Conference on 3D Vision.
[29] Ben Graham,et al. Sparse 3D convolutional neural networks , 2015, BMVC.
[30] Dushyant Rao,et al. Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[31] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Jing Huang,et al. Point cloud matching based on 3D self-similarity , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[33] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Ulrich Neumann,et al. 3D point cloud object detection with multi-view convolutional neural network , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[35] Leonidas J. Guibas,et al. A scalable active framework for region annotation in 3D shape collections , 2016, ACM Trans. Graph..
[36] Ulrich Neumann,et al. Fast and Robust Multi-view 3D Object Recognition in Point Clouds , 2015, 2015 International Conference on 3D Vision.
[37] Jing Huang,et al. Vehicle detection in urban point clouds with orthogonal-view convolutional neural network , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[38] Leonidas J. Guibas,et al. FPNN: Field Probing Neural Networks for 3D Data , 2016, NIPS.