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https://github.com/kaiwangm/awesome-deep-point-cloud-compression

A list of papers about deep point cloud compression.
https://github.com/kaiwangm/awesome-deep-point-cloud-compression

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A list of papers about deep point cloud compression.

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#  awesome-deep-point-cloud-compression 



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## Papers



### 2024



- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10445884)] Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction.



- [[VCIP](https://ieeexplore.ieee.org/document/10402752)] Adaptive Entropy Coding of Graph Transform Coefficients for Point Cloud Attribute Compression.



- [[arxiv](https://arxiv.org/abs/2404.07698)] Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator.



- [[arxiv](https://arxiv.org/abs/2404.06936)] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression. [[Pytorch](https://github.com/I2-Multimedia-Lab/PoLoPCAC)]



- [[MMVE](https://dl.acm.org/doi/abs/10.1145/3652212.3652217)] Progressive Coding for Deep Learning based Point Cloud Attribute Compression.



- [[TMM](https://ieeexplore.ieee.org/abstract/document/10487884)] Multi-Space Point Geometry Compression with Progressive Relation-Aware Transformer.



- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10448389)] Efficient Point Cloud Attribute Compression Using Rich Parallelizable Context Model.



- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10445988)] Efficient Point Cloud Attribute Compression Framework using Attribute-Guided Graph Fourier Transform.



- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10447944)] ScanPCGC: Learning-Based Lossless Point Cloud Geometry Compression using Sequential Slice Representation Encoding Auxiliary Information to Restore Compressed Point Cloud Geometry.



- [[IET](https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13139)] Point cloud geometry compression with sparse cascaded residuals and sparse attention.



- [[ICASSP](https://ieeexplore.ieee.org/document/10446596)] NeRI: Implicit Neural Representation of LiDAR Point Cloud Using Range Image Sequence. [[Pytorch](https://github.com/RuixiangXue/NeRI)]



- [[TVCG](https://ieeexplore.ieee.org/document/10470357)] Learning to Restore Compressed Point Cloud Attribute: A Fully Data-Driven Approach and A Rules-Unrolling-Based Optimization.



### 2023



- [[CVPR](https://openaccess.thecvf.com/content/CVPR2023/html/Song_Efficient_Hierarchical_Entropy_Model_for_Learned_Point_Cloud_Compression_CVPR_2023_paper.html)] Efficient Hierarchical Entropy Model for Learned Point Cloud Compression.



- [[TMM](https://doi.org/10.1109/TMM.2023.3331584)] Scalable Point Cloud Attribute Compression.



- [[arxiv](https://doi.org/10.48550/arXiv.2303.06519)] Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model.



- [[ICASSP](https://ieeexplore.ieee.org/document/10095385)] Deep probabilistic model for lossless scalable point cloud attribute compression. [[Pytorch](https://github.com/Weafre/MNeT/)]



- [[DCC](https://ieeexplore.ieee.org/abstract/document/10125514)] Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction.



- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10096559)] Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention.



- [[ACM MM](https://dl.acm.org/doi/abs/10.1145/3581783.3613793)] Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block.



- [[ICASSP](https://ieeexplore.ieee.org/document/10096294/)] Normalizing Flow Based Point Cloud Attribute Compression.



- [[APSIPA ASC](https://ieeexplore.ieee.org/document/10317255)] Sparse Tensor-based point cloud attribute compression using Augmented Normalizing Flows.



- [[ACM MM](https://dl.acm.org/doi/abs/10.1145/3581783.3612422)] PDE-based Progressive Prediction Framework for Attribute Compression of 3D Point Clouds. [[C++](https://github.com/Yanggoo/PDE-basedPointCloudCompression)]



- [[TIP](https://ieeexplore.ieee.org/document/10314418)] GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute. [[Pytorch](https://github.com/xjr998/GQE-Net)]



- [[arixiv](https://arxiv.org/abs/2311.13539)] Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression.



- [[TIP](https://ieeexplore.ieee.org/document/10234082)] Near-Lossless Compression of Point Cloud Attribute Using Quantization Parameter Cascading and Rate-Distortion Optimization.



- [[TPAMI](https://ieeexplore.ieee.org/document/10301698)] 3-D Point Cloud Attribute Compression With -Laplacian Embedding Graph Dictionary Learning.



- [[TVCG](https://ieeexplore.ieee.org/document/10328911)] GRNet: Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling. [[Pytorch](https://github.com/3dpcc/GRNet)]



- [[CVM](https://arxiv.org/abs/2209.08276)] ARNet: Compression Artifact Reduction for Point Cloud Attribute. [[Pytorch](https://github.com/3dpcc/ARNet)]



- [[TMM](https://ieeexplore.ieee.org/document/10313579)] ScalablePCAC: Scalable Point Cloud Attribute Compression.



- [[ACM MM](https://dl.acm.org/doi/10.1145/3581783.3613847)] YOGA: Yet Another Geometry-based Point Cloud Compressor. [[Pytorch](https://github.com/3dpcc/YOGAv1)]



- [[unpublished](https://3dpcc.github.io/publication/YOGAv2/)] YOGAv2: A Layered Point Cloud Compressor.



### 2022



- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9852261)] Isolated Points Prediction via Deep Neural Network on Point Cloud Lossless Geometry Compression.



- [[ARXIV](https://arxiv.org/abs/2208.12573)] Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention.



- [[ARXIV](https://arxiv.org/abs/2208.02519)] IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression. [[Pytorch](https://github.com/I2-Multimedia-Lab/IPDAE)]



- [[ICME](https://ieeexplore.ieee.org/abstract/document/9859853)] TDRNet: Transformer-Based Dual-Branch Restoration Network for Geometry Based Point Cloud Compression Artifacts.



- [[ECCV](https://arxiv.org/abs/2205.00760)] Point Cloud Compression with Sibling Context and Surface Priors. [[Pytorch](https://github.com/zlichen/PCC-S)]



- [[APCCPA](https://arxiv.org/abs/2209.04401)] GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression. [[Pytorch](https://github.com/InterDigitalInc/GRASP-Net)]



- [[AAAI](https://arxiv.org/abs/2202.06028)] OctAttention: Octree-based Large-scale Context Model for Point Cloud Compression. [[Pytorch](https://github.com/zb12138/OctAttention)]



- [[CVPR](http://arxiv.org/abs/2204.12684)] Density-preserving Deep Point Cloud Compression. [[Pytorch](https://github.com/yunhe20/D-PCC)]



- [[CVPR](https://arxiv.org/abs/2203.09931)] 3DAC: Learning Attribute Compression for Point Clouds. [[Pytorch](https://github.com/fatPeter/ThreeDAC)]



- [[ICMR](https://dl.acm.org/doi/abs/10.1145/3512527.3531423)] TransPCC: Towards Deep Point Cloud Compression via Transformers. [[Pytorch](https://github.com/jokieleung/TransPCC)]



- [[APCCPA](https://dl.acm.org/doi/abs/10.1145/3552457.3555731)] Transformer and Upsampling-Based Point Cloud Compression. [[Pytorch](https://github.com/arsx958/PCT_PCC)]



### 2021



- [[MM Asia](https://dl.acm.org/doi/abs/10.1145/3469877.3490611)] Patch-Based Deep Autoencoder for Point Cloud Geometry Compression. [[Pytorch](https://github.com/I2-Multimedia-Lab/PCC_Patch)]



- [[TCSVT](https://ieeexplore.ieee.org/document/9321375)] Lossy Point Cloud Geometry Compression via End-to-End Learning.



- [[DCC](https://ieeexplore.ieee.org/document/9418789)] Multiscale Point Cloud Geometry Compression. [[Pytorch](https://github.com/NJUVISION/PCGCv2)] [[Presentation](https://sigport.org/documents/multiscale-point-cloud-geometry-compression)]

   

- [[DCC](https://ieeexplore.ieee.org/document/9418793)] Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression. [[Presentation](https://sigport.org/documents/point-ae-dcgan-deep-learning-model-3d-point-cloud-lossy-geometry-compression)]



- [[CVPR](https://arxiv.org/abs/2105.02158)] VoxelContext-Net: An Octree based Framework for Point Cloud Compression. 



- [[ICASPP](https://ieeexplore.ieee.org/document/9414763)] Learning-Based Lossless Compression of 3D Point Cloud Geometry. [[Tensorflow](https://github.com/Weafre/VoxelDNN)]



- [[RAL-ICRA](https://ieeexplore.ieee.org/document/9354895)] Deep Compression for Dense Point Cloud Maps. [[Pytorch](https://github.com/PRBonn/deep-point-map-compression)]



- [[arXiv](https://arxiv.org/abs/2104.09859)] Multiscale deep context modeling for lossless point cloud geometry compression. [[Pytorch](https://github.com/Weafre/MSVoxelDNN)]



- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9496667)] Lossless Coding of Point Cloud Geometry using a Deep Generative Model. [[Tensorflow](https://github.com/Weafre/VoxelDNN_v2)]

  

- [[ICIP](https://ieeexplore.ieee.org/document/9506631)] Point Cloud Geometry Compression Via Neural Graph Sampling.



### 2020



- [[ICME](https://ieeexplore.ieee.org/document/9102866)] Lossy Geometry Compression Of 3d Point Cloud Data Via An Adaptive Octree-Guided Network. [[Tensorflow](https://github.com/wxz1996/pc_compress)]



- [[MMSP](https://ieeexplore.ieee.org/document/9287077)] Improved Deep Point Cloud Geometry Compression. [[Tensorflow](https://github.com/mauriceqch/pcc_geo_cnn_v2)]



- [[CVPR](https://ieeexplore.ieee.org/document/9157381)] OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression.



- [[NIPS](https://arxiv.org/abs/2011.07590)] MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models.



- [[ICIP](https://ieeexplore.ieee.org/document/9191180)] Folding-Based Compression Of Point Cloud Attributes. [[Tensorflow](https://github.com/mauriceqch/pcc_attr_folding)]



- [[ICIP](https://ieeexplore.ieee.org/document/9190647)] A Syndrome-Based Autoencoder For Point Cloud Geometry Compression.



### 2019



- [[ICIP](https://ieeexplore.ieee.org/document/8803413)] Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression. [[Tensorflow](https://github.com/mauriceqch/pcc_geo_cnn)]



- [[ICRA](https://ieeexplore.ieee.org/document/8794264)] Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks. [[PyTorch](https://github.com/ChenxiTU/Point-cloud-compression-by-RNN)]



- [[PCS](https://ieeexplore.ieee.org/document/8954537)] Point cloud coding: Adopting a deep learning-based approach. 



- [[arXiv](https://arxiv.org/abs/1909.12037)] Learned point cloud geometry compression.



- [[arXiv](https://arxiv.org/abs/1905.03691)] Deep autoencoder-based lossy geometry compression for point clouds.  [[Tensorflow](https://github.com/YanWei123/Deep-AutoEncoder-based-Lossy-Geometry-Compression-for-Point-Clouds)]



- [[CMM](https://dl.acm.org/doi/10.1145/3343031.3351061)] 3d point cloud geometry compression on deep learning.



- [[TIP](https://ieeexplore.ieee.org/document/8676054)] A Volumetric Approach to Point Cloud Compression—Part I: Attribute Compression.



- [[TIP](https://ieeexplore.ieee.org/document/8931233)] A Volumetric Approach to Point Cloud Compression–Part II: Geometry Compression.



### 2018



- [[MM](https://dl.acm.org/doi/10.1145/3240508.3240696)] Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction.



### 2016



- [[MM](https://ieeexplore.ieee.org/document/7405340)] Graph-based compression of dynamic 3D point cloud sequences.



## Non-Deep Learning Methods and Library



- [[Draco](https://github.com/google/draco)] Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.



- [[MPEG V-PCC](https://github.com/MPEGGroup/mpeg-pcc-tmc2)] MPEG Video codec based point cloud compression (V-PCC) test model (tmc2).



- [[MPEG G-PCC](https://github.com/MPEGGroup/mpeg-pcc-tmc13)] MPEG Geometry based point cloud compression (G-PCC) test model (tmc13).



- [[CAS '18](https://ieeexplore.ieee.org/document/8571288)] Emerging MPEG Standards for Point Cloud Compression.



- [[EG '06](https://dl.acm.org/doi/10.5555/2386388.2386404)] Octree-based point-cloud compression.



- [[ICRA '12](https://ieeexplore.ieee.org/document/6224647)] Real-time compression of point cloud streams.



### 2016



- [[MM](https://ieeexplore.ieee.org/document/7482691)] Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.



### 2018



- [[ICIP](https://ieeexplore.ieee.org/document/8451802)] Intra-Frame Context-Based Octree Coding for Point-Cloud Geometry.



### 2020



- [[IROS](https://ieeexplore.ieee.org/document/9341071)] Real-Time Spatio-Temporal LiDAR Point Cloud Compression. [[C++ '1](https://github.com/yaoli1992/LiDAR-Point-Cloud-Compression)] [[C++ '2](https://github.com/horizon-research/Real-Time-Spatio-Temporal-LiDAR-Point-Cloud-Compression)]



### 2021



- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9503405)] Lossy Point Cloud Geometry Compression via Region-wise Processing.



## Datasets



- [[KITTI](http://www.cvlibs.net/datasets/kitti/)] The KITTI Vision Benchmark Suite.



- [[ShapeNet](https://shapenet.org/)] A collaborative dataset between researchers at Princeton, Stanford and TTIC.



- [[ModelNet](https://modelnet.cs.princeton.edu/)] ModelNet Database.



- [[JPEG Pleno](http://plenodb.jpeg.org/)] JPEG Pleno Database.



- [[MVUB](http://plenodb.jpeg.org/pc/microsoft/)] Microsoft Voxelized Upper Bodies dataset.