3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
暂无分享,去创建一个
[1] 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).
[2] Stefan Leutenegger,et al. SemanticFusion: Dense 3D semantic mapping with convolutional neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[3] Yang Liu,et al. O-CNN , 2017, ACM Trans. Graph..
[4] Olaf Kähler,et al. Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices , 2015, IEEE Transactions on Visualization and Computer Graphics.
[5] Jitendra Malik,et al. Learning a Multi-View Stereo Machine , 2017, NIPS.
[6] Bastian Leibe,et al. Dense 3D semantic mapping of indoor scenes from RGB-D images , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[7] Jitendra Malik,et al. Hierarchical Surface Prediction for 3D Object Reconstruction , 2017, 2017 International Conference on 3D Vision (3DV).
[8] Marc Levoy,et al. A volumetric method for building complex models from range images , 1996, SIGGRAPH.
[9] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] 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).
[11] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[12] 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).
[13] Eugenio Culurciello,et al. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation , 2016, ArXiv.
[14] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[15] Lu Fang,et al. SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Silvio Savarese,et al. 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction , 2016, ECCV.
[17] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[18] Matthias Nießner,et al. Matterport3D: Learning from RGB-D Data in Indoor Environments , 2017, 2017 International Conference on 3D Vision (3DV).
[19] Thomas A. Funkhouser,et al. Semantic Scene Completion from a Single Depth Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Horst Bischof,et al. OctNetFusion: Learning Depth Fusion from Data , 2017, 2017 International Conference on 3D Vision (3DV).
[21] 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).
[22] Nathan Silberman,et al. Indoor scene segmentation using a structured light sensor , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[23] Matthias Nießner,et al. ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Roberto Cipolla,et al. SceneNet: Understanding Real World Indoor Scenes With Synthetic Data , 2015, ArXiv.
[25] James M. Rehg,et al. Joint Semantic Segmentation and 3D Reconstruction from Monocular Video , 2014, ECCV.
[26] Roberto Cipolla,et al. Understanding RealWorld Indoor Scenes with Synthetic Data , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Matthias Nießner,et al. SemanticPaint , 2015, ACM Trans. Graph..
[28] Kaiming He,et al. Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[29] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[30] Alexei A. Efros,et al. Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Zhen Li,et al. High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[32] Matthias Nießner,et al. Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Matthias Nießner,et al. Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..
[34] Andrew W. Fitzgibbon,et al. KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.
[35] Marc Pollefeys,et al. Joint 3D Scene Reconstruction and Class Segmentation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[36] 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).
[37] Marc Pollefeys,et al. Discrete optimization of ray potentials for semantic 3D reconstruction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Matthias Nießner,et al. BundleFusion , 2016, TOGS.
[39] Patrick Pérez,et al. Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[40] Subhransu Maji,et al. 3D Shape Segmentation with Projective Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Thomas Brox,et al. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[43] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[44] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).