PatchCNN: An Explicit Convolution Operator for Point Clouds Perception

[1]  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).

[2]  Ulrich Neumann,et al.  Recurrent Slice Networks for 3D Segmentation of Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  M. Disney,et al.  Leaf and wood classification framework for terrestrial LiDAR point clouds , 2019, Methods in Ecology and Evolution.

[4]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[5]  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).

[6]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[7]  Markus H. Gross,et al.  PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks , 2018, Comput. Graph. Forum.

[8]  Feng Wu,et al.  Background Prior-Based Salient Object Detection via Deep Reconstruction Residual , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Timo Ropinski,et al.  Monte Carlo convolution for learning on non-uniformly sampled point clouds , 2018, ACM Trans. Graph..

[11]  Fuxin Li,et al.  PointConv: Deep Convolutional Networks on 3D Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Vladlen Koltun,et al.  Tangent Convolutions for Dense Prediction in 3D , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Yaron Lipman,et al.  Point convolutional neural networks by extension operators , 2018, ACM Trans. Graph..

[16]  Jean-Michel Guldmann,et al.  Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach , 2019, Comput. Environ. Urban Syst..

[17]  Leif Kobbelt,et al.  A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization , 2019, Comput. Graph. Forum.

[18]  Matthias Nießner,et al.  ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Cewu Lu,et al.  PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation , 2018, ArXiv.

[20]  Daniela Rus,et al.  Learning Object Grasping for Soft Robot Hands , 2018, IEEE Robotics and Automation Letters.

[21]  Yifan Xu,et al.  SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters , 2018, ECCV.

[22]  A. Vetrivel,et al.  Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[23]  Wei Wu,et al.  PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.