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PointCloudPapers

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The papers of point cloud.

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# Papers of Point Cloud

## Classification, Segmentation or Representation Learning

* [2017 CVPR] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [Paper](https://arxiv.org/pdf/1612.00593) [Code1](https://github.com/charlesq34/pointnet) [Code2](https://github.com/fxia22/pointnet.pytorch)
* [2017 NIPS] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space [Paper](https://arxiv.org/pdf/1706.02413.pdf) [Code1](https://github.com/charlesq34/pointnet2) [Code2](https://github.com/yanx27/Pointnet_Pointnet2_pytorch)
* [2017 CVPR] Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs [Paper](https://arxiv.org/pdf/1704.02901.pdf) [Code](https://github.com/mys007/ecc)
* [2017 CVPR] Dynamic Filters in Graph Convolutional Networks [Paper](https://openaccess.thecvf.com/content_cvpr_2017/papers/Simonovsky_Dynamic_Edge-Conditioned_Filters_CVPR_2017_paper.pdf)
* [2018 NIPS] PointCNN：Convolution On X-Transformed Points [Paper](https://arxiv.org/pdf/1801.07791.pdf) [Code](https://github.com/yangyanli/PointCNN)
* [2018 CVPR] SO-Net: Self-Organizing Network for Point Cloud Analysis [Paper](https://arxiv.org/pdf/1803.04249.pdf) [Code](https://github.com/lijx10/SO-Net)
* [2019 CVPR] DGCNN: Dynamic Graph CNN for Learning on Point Clouds [Paper](https://arxiv.org/pdf/1801.07829.pdf) [Code](https://github.com/WangYueFt/dgcnn)
* [2019 CVPR] PointConv：Deep Convolutional Networks on 3D Point Clouds [Paper](https://arxiv.org/pdf/1811.07246.pdf) [Code1](https://github.com/DylanWusee/pointconv) [Code2](https://github.com/DylanWusee/pointconv_pytorch)
* [2019 CVPR] PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing [Paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_PointWeb_Enhancing_Local_Neighborhood_Features_for_Point_Cloud_Processing_CVPR_2019_paper.pdf) [Code](https://github.com/hszhao/PointWeb)
* [2019 CVPR] Relation-Shape Convolutional Neural Network for Point Cloud Analysis [Paper](https://arxiv.org/pdf/1904.07601.pdf) [Code](https://github.com/Yochengliu/Relation-Shape-CNN)
* [2019 ICCV] KPConv: Flexible and Deformable Convolution for Point Clouds [Paper](https://arxiv.org/pdf/1904.08889.pdf) [Code](https://github.com/HuguesTHOMAS/KPConv)
* [2019 NIPS] Point-Voxel CNN for Efficient 3D Deep Learning [Paper](https://arxiv.org/1907.03739) [Code](https://github.com/mit-han-lab/pvcnn)
* [2020 CVPR] Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds [Paper](https://arxiv.org/pdf/2003.12971.pdf) [Code](https://github.com/raoyongming/PointGLR)
* [2020 CVPR] PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [Paper](https://arxiv.org/abs/2003.00492) [Code](https://github.com/yanx27/PointASNL)
* [2020 CVM] Point Cloud Transformer [Paper](https://arxiv.org/pdf/2012.09688) [Code](https://github.com/MenghaoGuo/PCT)
* [2021 ICCV] Point Transformer [Paper](https://arxiv.org/abs/2012.09164) [Code](https://github.com/lucidrains/point-transformer-pytorch)

## Upsampling

* [2018 CVPR] PU-Net: Point Cloud Upsampling Network [Paper](https://arxiv.org/pdf/1801.06761.pdf) [Code](https://github.com/yulequan/PU-Net)
* [2019 ICCV] PU-GAN: a Point Cloud Upsampling Adversarial Network [Paper](https://arxiv.org/abs/1907.10844) [Code](https://github.com/liruihui/PU-GAN)

## Generation

* [2018 ICML] Learning Representations and Generative Models for 3D Point Clouds [Paper](https://arxiv.org/abs/1707.02392) [Code](https://github.com/optas/latent_3d_points)
* [2018 CVPR] FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation [Paper](https://arxiv.org/pdf/1712.07262.pdf) [Code](https://github.com/AnTao97/UnsupervisedPointCloudReconstruction)
* [2019 CVPR] 3D Point Capsule Networks [Paper](https://arxiv.org/pdf/1812.10775.pdf) [Code](https://github.com/yongheng1991/3D-point-capsule-networks)
* [2019 ICCV] 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions [Paper](https://arxiv.org/abs/1905.06292)
* [2019 ICCV] PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows [Paper](https://arxiv.org/abs/2103.06564) [Code](https://github.com/lxtGH/PFSegNets)
* [2020 ECCV] Discrete Point Flow Networks for Efficient Point Cloud Generation [Paper](https://arxiv.org/abs/2007.10170) [Code](https://github.com/Regenerator/dpf-nets)
* [2020 NIPS] SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds [Paper](https://arxiv.org/abs/2006.04604) [Code](https://github.com/ANLGBOY/SoftFlow)
* [2021 CVPR] Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper](https://arxiv.org/abs/2103.01458) [Code](https://github.com/luost26/diffusion-point-cloud)
* [2021 ICCV] 3D Shape Generation and Completion through Point-Voxel Diffusion [Paper](https://arxiv.org/abs/2104.03670)

## Completion

### Dataset

* [PCN](https://drive.google.com/drive/folders/1M_lJN14Ac1RtPtEQxNlCV9e8pom3U6Pa)
* [Completion3D](https://completion3d.stanford.edu/)
* [MVP](https://github.com/paul007pl/MVP_Benchmark/tree/main/completion)
* [PCL2PCL (code: npaj)](https://pan.baidu.com/share/init?surl=jDJJ6RjRpuXpu5GSJcPQmg)
* [KITTI (code: npaj)](https://pan.baidu.com/share/init?surl=jDJJ6RjRpuXpu5GSJcPQmg)
* [ScanNet (code: npaj)](https://pan.baidu.com/share/init?surl=jDJJ6RjRpuXpu5GSJcPQmg)
* [Materport3D (code: npaj)](https://pan.baidu.com/share/init?surl=jDJJ6RjRpuXpu5GSJcPQmg)

### Paired

* [2018 3DV] PCN: Point Completion Network [Paper](https://arxiv.org/pdf/1808.00671.pdf) [Code](https://github.com/wentaoyuan/pcn)
* [2019 CVPR] TopNet: Structural Point Cloud Decoder [Paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.pdf) [Code](https://github.com/lynetcha/completion3d)
* [2020 IROS] ECG: Edge-aware Point Cloud Completion with Graph Convolution [Paper](http://ras.papercept.net/images/temp/IROS/files/2954.pdf)
* [2020 CVPR] PF-Net: Point Fractal Network for 3D Point Cloud Completion [Paper](https://arxiv.org/pdf/2003.00410.pdf) [Code](https://github.com/zztianzz/PF-Net-Point-Fractal-Network)
* [2020 CVPR] Morphing and Sampling Network for Dense Point Cloud Completion [Paper](https://arxiv.org/pdf/1912.00280.pdf) [Code](https://github.com/Colin97/MSN-Point-Cloud-Completion)
* [2020 CVPR] Cascaded Refinement Network for Point Cloud Completion [Paper](https://arxiv.org/pdf/2004.03327.pdf) [Code](https://github.com/xiaogangw/cascaded-point-completion)
* [2020 CVPR] Point Cloud Completion by Skip-attention Network with Hierarchical Folding [Paper](https://arxiv.org/pdf/2005.03871) [Code](https://github.com/RaminHasibi/SA_Net)
* [2020 NIPS] Skeleton-bridged Point Completion: From Global Inference to Local Adjustment [Paper](https://arxiv.org/abs/2010.07428)
* [2020 ECCV] Detail Preserved Point Cloud Completion via Separated Feature Aggregation [Paper](https://arxiv.org/pdf/2007.02374.pdf) [Code](https://github.com/XLechter/Detail-Preserved-Point-Cloud-Completion-via-SFA)
* [2020 ECCV] GRNet: Gridding Residual Network for Dense Point Cloud Completion [Paper](https://arxiv.org/pdf/2006.03761.pdf) [Code](https://github.com/hzxie/GRNet)
* [2021 AAAI] ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for RobustPoint Cloud Completion [Paper](https://ojs.aaai.org/index.php/AAAI/article/view/16478/16285)
* [2021 CVPR] PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths [Papre](https://arxiv.org/abs/2012.03408) [Code](https://github.com/diviswen/PMP-Net)
* [2021 CVPR] Variational Relational Point Completion Network [Paper](https://arxiv.org/abs/2104.10154) [Code](https://github.com/paul007pl/VRCNet)
* [2021 CVPR] Denoise and Contrast for Category Agnostic Shape Completion [Paper](https://arxiv.org/abs/2103.16671) [Code](https://github.com/antoalli/Deco)
* [2021 CVPR] View-Gauided Point Cloud Completion [Paper](https://arxiv.org/abs/2104.05666)
* [2021 ICCV] PoinTr: Diverse Piont Cloud Completion with Geometry-Aware Transformers [Paper](https://arxiv.org/abs/2108.08839) [Code](https://github.com/yuxumin/PoinTr)
* [2021 ICCV] SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [Paper](https://arxiv.org/abs/2108.04444) [Code](https://github.com/allenxiangx/snowflakenet)
* [2021 ACM MM] ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion [Paper](https://arxiv.org/abs/2104.09587)
* [2022 AAAI] Attention-based Transformation from Latent Features to Point Clouds [Paper](https://arxiv.org/abs/2112.05324)
* [2022] A Conditional Point Diffusion-Refinement Paradigm For 3D Point Cloud Completion [Paper](https://arxiv.org/abs/2112.03530)
* [2022] High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling [Paper](https://arxiv.org/abs/2112.11271)

### Unpaired

* [2020 ICLR] Unpaired Point Cloud Completion on Real Scans Using Adversarial Training [Paper](https://arxiv.org/abs/1904.00069) [Code](https://github.com/xuelin-chen/pcl2pcl-gan-pub)
* [2021 CVPR] Unsupervised 3D Shape Completion through GAN Inversion [Paper](https://arxiv.org/abs/2104.13366) [Code](https://github.com/junzhezhang/shape-inversion)
* [2021 CVPR] Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding [Paper](https://arxiv.org/abs/2103.07838) [Code](https://github.com/diviswen/Cycle4Completion)
* [2021 ArXiv] MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching [Paper](https://arxiv.org/abs/2111.11976)

## 3D Object Detection

* [2018 CVPR] Frustum PointNets for 3D Object Detection from RGB-D Data [Paper](https://arxiv.org/pdf/1711.08488.pdf) [Code](https://github.com/charlesq34/frustum-pointnets)
* [2020 CVPR] Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [Paper](https://arxiv.org/pdf/2003.01251) [Code](https://github.com/WeijingShi/Point-GNN)
* [2021 AAAI] PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [Paper](https://arxiv.org/pdf/2012.10412)

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