4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
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[1] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Benjamin Graham,et al. Spatially-sparse convolutional neural networks , 2014, ArXiv.
[3] Silvio Savarese,et al. SEGCloud: Semantic Segmentation of 3D Point Clouds , 2017, 2017 International Conference on 3D Vision (3DV).
[4] Xiang Li,et al. Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN) , 2018, MICCAI.
[5] Andrew Adams,et al. Fast High‐Dimensional Filtering Using the Permutohedral Lattice , 2010, Comput. Graph. Forum.
[6] Laurens van der Maaten,et al. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Xin Tong,et al. PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Peter V. Gehler,et al. Efficient 2D and 3D Facade Segmentation Using Auto-Context , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Luc Van Gool,et al. 3D all the way: Semantic segmentation of urban scenes from start to end in 3D , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[11] Vladlen Koltun,et al. Tangent Convolutions for Dense Prediction in 3D , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[12] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[13] Baoquan Chen,et al. PointCNN: Convolution On $\mathcal{X}$-Transformed Points , 2018 .
[14] Matthias Nießner,et al. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Matthias Nießner,et al. 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation , 2018, ECCV.
[17] Antonio M. López,et al. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Vladlen Koltun,et al. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.
[19] Silvio Savarese,et al. 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction , 2016, ECCV.
[20] Daniel Rueckert,et al. Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm , 2003, MICCAI.
[21] Timo Ropinski,et al. Monte Carlo convolution for learning on non-uniformly sampled point clouds , 2018, ACM Trans. Graph..
[22] Matthias Nießner,et al. 3DMatch: Learning the Matching of Local 3D Geometry in Range Scans , 2016, ArXiv.
[23] Silvio Savarese,et al. 3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Subhransu Maji,et al. SPLATNet: Sparse Lattice Networks for Point Cloud Processing , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Ben Graham,et al. Sparse 3D convolutional neural networks , 2015, BMVC.
[26] Parker Allen Tew,et al. An investigation of sparse tensor formats for tensor libraries , 2016 .
[27] Demetri Terzopoulos,et al. A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[28] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[29] Raquel Urtasun,et al. Deep Parametric Continuous Convolutional Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Luc Van Gool,et al. Learning Where to Classify in Multi-view Semantic Segmentation , 2014, ECCV.
[31] 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).
[32] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[33] Martin Simonovsky,et al. Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[34] Nir Friedman,et al. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning , 2009 .
[35] Jiamao Li,et al. 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation , 2018, ECCV.