PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

[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).