PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
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[1] Jiwen Lu,et al. DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[2] Sanja Fidler,et al. 3D Graph Neural Networks for RGBD Semantic Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[3] Zheng Zhang,et al. A Closer Look at Local Aggregation Operators in Point Cloud Analysis , 2020, ECCV.
[4] Silvio Savarese,et al. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Ajmal Mian,et al. SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Michael Felsberg,et al. Deep Projective 3D Semantic Segmentation , 2017, CAIP.
[7] Xizhou Zhu,et al. Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation , 2020, ICLR.
[8] Yifan Xu,et al. SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters , 2018, ECCV.
[9] Leonidas J. Guibas,et al. A scalable active framework for region annotation in 3D shape collections , 2016, ACM Trans. Graph..
[10] Song Han,et al. Point-Voxel CNN for Efficient 3D Deep Learning , 2019, NeurIPS.
[11] Shu Liu,et al. CN: Channel Normalization for Point Cloud Recognition , 2020, European Conference on Computer Vision.
[12] Yaron Lipman,et al. Point convolutional neural networks by extension operators , 2018, ACM Trans. Graph..
[13] Quoc V. Le,et al. CondConv: Conditionally Parameterized Convolutions for Efficient Inference , 2019, NeurIPS.
[14] Jing Hua,et al. A-CNN: Annularly Convolutional Neural Networks on Point Clouds , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Ulrich Neumann,et al. Recurrent Slice Networks for 3D Segmentation of Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[16] Leonidas J. Guibas,et al. SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Nikos Komodakis,et al. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Shiming Xiang,et al. Relation-Shape Convolutional Neural Network for Point Cloud Analysis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Dong Tian,et al. Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[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] Xiaogang Wang,et al. Interpolated Convolutional Networks for 3D Point Cloud Understanding , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[23] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[24] Vladlen Koltun,et al. Tangent Convolutions for Dense Prediction in 3D , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Xiaojuan Qi,et al. Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud , 2020, AAAI.
[26] Qing Li,et al. Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling , 2019, AAAI.
[27] Anath Fischer,et al. 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks , 2018, IEEE Robotics and Automation Letters.
[28] Leonidas J. Guibas,et al. Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[29] Zhipeng Zhou,et al. Geometry Sharing Network for 3D Point Cloud Classification and Segmentation , 2019, AAAI.
[30] 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).
[31] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[32] Wei Wu,et al. PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.
[33] Lin Gao,et al. VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Alexandre Boulch,et al. Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks , 2017, 3DOR@Eurographics.
[35] 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.
[36] Subhransu Maji,et al. SPLATNet: Sparse Lattice Networks for Point Cloud Processing , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] C. Qi. Deep Learning on Point Sets for 3 D Classification and Segmentation , 2016 .
[38] Bo Yang,et al. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Kaleem Siddiqi,et al. Local Spectral Graph Convolution for Point Set Feature Learning , 2018, ECCV.
[40] Patrick Wieschollek,et al. Flex-Convolution - Million-Scale Point-Cloud Learning Beyond Grid-Worlds , 2018, ACCV.
[41] Luc Van Gool,et al. Dynamic Filter Networks , 2016, NIPS.
[42] Ulrich Neumann,et al. Grid-GCN for Fast and Scalable Point Cloud Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Gernot Riegler,et al. OctNet: Learning Deep 3D Representations at High Resolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Raquel Urtasun,et al. Deep Parametric Continuous Convolutional Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Yu-Chiang Frank Wang,et al. Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Lei Wang,et al. Appendix for : Graph Attention Convolution for Point Cloud Semantic Segmentation , 2019 .
[47] Silvio Savarese,et al. 3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[49] Jiaxin Li,et al. SO-Net: Self-Organizing Network for Point Cloud Analysis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[50] Silvio Savarese,et al. SEGCloud: Semantic Segmentation of 3D Point Clouds , 2017, 2017 International Conference on 3D Vision (3DV).
[51] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[52] Chi-Wing Fu,et al. Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[53] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[54] Fuxin Li,et al. PointConv: Deep Convolutional Networks on 3D Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Yu Qiao,et al. Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud , 2021, AAAI.
[56] Matthias Zwicker,et al. Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network , 2018, AAAI.
[57] Binh-Son Hua,et al. Pointwise Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[58] Shuguang Cui,et al. FPConv: Learning Local Flattening for Point Convolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Leonidas J. Guibas,et al. KPConv: Flexible and Deformable Convolution for Point Clouds , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[60] RusuRadu Bogdan,et al. Towards 3D Point cloud based object maps for household environments , 2008 .
[61] 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).