{"id":19105,"url":"https://github.com/kaiwangm/awesome-learned-point-cloud-compression","name":"awesome-learned-point-cloud-compression","description":"A list of papers about learned point cloud compression.","projects_count":145,"last_synced_at":"2026-08-14T08:00:17.468Z","repository":{"id":46652337,"uuid":"375102325","full_name":"kaiwangm/awesome-learned-point-cloud-compression","owner":"kaiwangm","description":"A list of papers about learned point cloud compression.","archived":false,"fork":false,"pushed_at":"2025-10-25T12:38:24.000Z","size":102,"stargazers_count":138,"open_issues_count":0,"forks_count":11,"subscribers_count":7,"default_branch":"main","last_synced_at":"2026-07-25T22:05:32.629Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kaiwangm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-06-08T18:07:28.000Z","updated_at":"2026-04-20T06:48:47.000Z","dependencies_parsed_at":"2024-10-06T02:10:18.947Z","dependency_job_id":"18f68e4b-2004-4207-a4c9-77e451cc6e5a","html_url":"https://github.com/kaiwangm/awesome-learned-point-cloud-compression","commit_stats":null,"previous_names":["kaiwangm/awesome-learned-point-cloud-compression","kaiwangm/awesome-deep-point-cloud-compression"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kaiwangm/awesome-learned-point-cloud-compression","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kaiwangm%2Fawesome-learned-point-cloud-compression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kaiwangm%2Fawesome-learned-point-cloud-compression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kaiwangm%2Fawesome-learned-point-cloud-compression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kaiwangm%2Fawesome-learned-point-cloud-compression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kaiwangm","download_url":"https://codeload.github.com/kaiwangm/awesome-learned-point-cloud-compression/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kaiwangm%2Fawesome-learned-point-cloud-compression/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36637662,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"online","status_checked_at":"2026-08-14T02:00:06.934Z","response_time":54,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T12:55:30.873Z","updated_at":"2026-08-14T08:00:17.469Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Datasets","Papers","Others"],"sub_categories":["2021","2022","2016","2019","2020","2018","2024","2023","2025","2026"],"readme":"#  awesome-learned-point-cloud-compression \n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\n[![PR's Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com) \n\n## Papers\n\n### 2026\n\n- [[arxiv](https://arxiv.org/abs/2605.18006)] Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression. [[Code](https://github.com/SDUChangSun/Inter-LPCM)]\n\n- [[arxiv](https://arxiv.org/abs/2605.01320)] PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression.\n\n- [[ICIP](https://arxiv.org/abs/2604.28045)] TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement.\n\n- [[arxiv](https://arxiv.org/abs/2604.04737)] LEAN-3D: Low-latency Hierarchical Point Cloud Codec for Mobile 3D Streaming.\n\n- [[arxiv](https://arxiv.org/abs/2603.28095)] Octree-based Learned Point Cloud Geometry Compression: A Lossy Perspective.\n\n- [[TIP](https://arxiv.org/abs/2603.25260)] Towards Practical Lossless Neural Compression for LiDAR Point Clouds. [[Code](https://github.com/pengpeng-yu/FastPCC)]\n\n- [[arxiv](https://arxiv.org/abs/2603.23162)] LiZIP: An Auto-Regressive Compression Framework for LiDAR Point Clouds. [[Code](https://github.com/HWUDLabAIRoboticsResearch/LiZIP)]\n\n- [[arxiv](https://arxiv.org/abs/2602.21662)] HybridINR-PCGC: Hybrid Lossless Point Cloud Geometry Compression Bridging Pretrained Model and Implicit Neural Representation.\n\n- [[arxiv](https://arxiv.org/abs/2602.00186)] SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation.\n\n- [[AAAI](https://arxiv.org/abs/2601.12255)] DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression. [[Code](https://github.com/zb12138/DeepRAHT)]\n\n- [[ACM TOMM](https://arxiv.org/abs/2601.12261)] DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression. [[Code](https://github.com/zb12138/DALD_PCAC)]\n\n- [[WACV](https://arxiv.org/abs/2512.22463)] MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression.\n\n- [[CVPR](https://arxiv.org/abs/2510.20331)] AnyPcc: Compressing Any Point Cloud with a Single Universal Model. [[Code](https://github.com/Wangkkklll/AnyPcc)]\n\n### 2025\n\n- [[ICML](https://arxiv.org/abs/2505.09433)] SerLiC: Efficient LiDAR Reflectance Compression via Scanning Serialization. [[Code](https://github.com/3dpcc/SerLiC)]\n\n- [[arxiv](https://arxiv.org/abs/2504.00481)] Hierarchical Attention Networks for Lossless Point Cloud Attribute Compression.\n\n- [[arxiv](https://arxiv.org/abs/2504.14240)] ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision.\n\n- [[arxiv](https://arxiv.org/abs/2502.17939)] Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Clouds. [[Code](https://github.com/ttlzfhy/Deep-JGAC)]\n\n- [[RA-L](https://ieeexplore.ieee.org/document/11206444)] Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression. [[Code](https://github.com/LTU-RAI/sga-dpcc.git)]\n\n- [[CVPR](https://openaccess.thecvf.com/content/CVPR2025/papers/You_RENO_Real-Time_Neural_Compression_for_3D_LiDAR_Point_Clouds_CVPR_2025_paper.pdf)] RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds. [[Code](https://github.com/NJUVISION/RENO)]\n\n- [[RA-L](https://ieeexplore.ieee.org/abstract/document/10876615)] H-PCC: Point Cloud Compression With Hybrid Mode Selection and Content Adaptive Down-Sampling.\n\n- [[TII](https://ieeexplore.ieee.org/abstract/document/10884627)] suLPCC: A Novel LiDAR Point Cloud Compression Framework for Scene Understanding Tasks.\n\n- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/10938715)] GAEM: Graph-Driven Attention-Based Entropy Model for LiDAR Point Cloud Compression.\n\n- [[CVM](https://ieeexplore.ieee.org/abstract/document/11174068)] PCAC-GAN: A sparse-tensor-based generative adversarial network for 3D point cloud attribute compression.\n\n- [[TIP](https://ieeexplore.ieee.org/abstract/document/10989632)] Advances in Predictive RAHT for Geometric Point Cloud Compression.\n\n- [[TIP](https://ieeexplore.ieee.org/abstract/document/11164976)] Structure-Aware Generative Point Cloud Compression for Visual Perception.\n\n- [[AAAI](https://ojs.aaai.org/index.php/AAAI/article/view/33439)] AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression.\n\n- [[AAAI](https://ojs.aaai.org/index.php/AAAI/article/view/33387)] UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach. [[Code](https://github.com/Wangkkklll/UniPCGC)]\n\n- [[ACM TOMM](https://dl.acm.org/doi/abs/10.1145/3715916)] Compression Approaches for LiDAR Point Clouds and Beyond: A Survey.\n\n### 2024\n\n- [[arxiv](https://arxiv.org/abs/2411.14501)] U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation.\n\n- [[arxiv](https://arxiv.org/abs/2411.07899)] Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer.\n\n- [[arxiv](https://arxiv.org/abs/2410.17823)] Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds. [[Code](https://github.com/I2-Multimedia-Lab/Att2CPC)]\n\n- [[arxiv](https://arxiv.org/abs/2409.12724)] PVContext: Hybrid Context Model for Point Cloud Compression.\n\n- [[ICIP](https://arxiv.org/abs/2408.10665)] End-to-end Learned Lossy Dynamic Point Cloud Attribute Compression.\n\n- [[arxiv](https://arxiv.org/abs/2408.10543)] Diff-PCC: Diffusion-based Neural Compression for 3D Point Clouds.\n\n- [[arxiv](https://arxiv.org/abs/2408.08682)] LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression.\n\n- [[arxiv](https://arxiv.org/abs/2408.00599)] Learned Compression of Point Cloud Geometry and Attributes in a Single Model through Multimodal Rate-Control.\n\n- [[arxiv](https://arxiv.org/abs/2406.05915)] Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering.\n\n- [[arxiv](https://arxiv.org/abs/2406.00791)] Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor.\n\n- [[arxiv](https://arxiv.org/abs/2405.11493)] Point Cloud Compression with Implicit Neural Representations: A Unified Framework.\n\n- [[arxiv](https://arxiv.org/abs/2403.08236)] Point Cloud Compression via Constrained Optimal Transport. [[Code](https://github.com/cognaclee/PCC-COT)]\n\n- [[PCS](https://arxiv.org/abs/2402.12532)] Scalable Human-Machine Point Cloud Compression.\n\n- [[3DV](https://arxiv.org/abs/2402.07243)] PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression.\n\n- [[AAAI](https://arxiv.org/abs/2308.12535)] SCP: Spherical-Coordinate-based Learned Point Cloud Compression. [[Code](https://github.com/luoao-kddi/SCP)]\n\n- [[TCSVT](https://arxiv.org/abs/2305.01309)] Geometric Prior Based Deep Human Point Cloud Geometry Compression.\n\n- [[ICASSP](https://arxiv.org/abs/2211.10916)] ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression.\n\n- [[TIP](https://arxiv.org/abs/2207.12554)] Inter-Frame Compression for Dynamic Point Cloud Geometry Coding.\n\n- [[TPAMI](https://ieeexplore.ieee.org/abstract/document/10682571)] A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part I: Geometry. [[Code](https://github.com/NJUVISION/Unicorn)]\n\n- [[TPAMI](https://ieeexplore.ieee.org/abstract/document/10682566)] A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part II: Attribute. [[Code](https://github.com/NJUVISION/Unicorn)]\n\n- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/10530090)] Content-aware Rate Control for Geometry-based Point Cloud Compression.\n\n- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10445884)] Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction.\n\n- [[VCIP](https://ieeexplore.ieee.org/abstract/document/10402752)] Adaptive Entropy Coding of Graph Transform Coefficients for Point Cloud Attribute Compression.\n\n- [[arxiv](https://arxiv.org/abs/2404.07698)] Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator.\n\n- [[arxiv](https://arxiv.org/abs/2404.06936)] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression. [[Code](https://github.com/I2-Multimedia-Lab/PoLoPCAC)]\n\n- [[MMVE](https://dl.acm.org/doi/abs/10.1145/3652212.3652217)] Progressive Coding for Deep Learning based Point Cloud Attribute Compression.\n\n- [[TMM](https://ieeexplore.ieee.org/abstract/document/10487884)] Multi-Space Point Geometry Compression with Progressive Relation-Aware Transformer.\n\n- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10448389)] Efficient Point Cloud Attribute Compression Using Rich Parallelizable Context Model.\n\n- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10445988)] Efficient Point Cloud Attribute Compression Framework using Attribute-Guided Graph Fourier Transform.\n\n- [[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.\n\n- [[IET](https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.13139)] Point cloud geometry compression with sparse cascaded residuals and sparse attention.\n\n- [[ICASSP](https://ieeexplore.ieee.org/document/10446596)] NeRI: Implicit Neural Representation of LiDAR Point Cloud Using Range Image Sequence. [[Code](https://github.com/RuixiangXue/NeRI)]\n\n- [[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.\n  \n- [[ECCV](https://arxiv.org/abs/2408.02966)] Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement. [[Code](https://github.com/hxu160/CRCIR_for_PCGC)]\n\n- [[IJCAI](https://www.ijcai.org/proceedings/2024/595)] Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned Geometry Codec for Large-Scale Point Cloud Scenes. [[Code](https://github.com/I2-Multimedia-Lab/Pointsoup)]\n\n### 2023\n\n- [[MMSP](https://arxiv.org/abs/2308.05959)] Learned Point Cloud Compression for Classification.\n\n- [[ICASSP](https://arxiv.org/abs/2303.04027)] BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression.\n\n- [[arxiv](https://arxiv.org/abs/2301.12165)] Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding.\n\n- [[TCSVT](https://arxiv.org/abs/2209.12512)] Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression.\n\n- [[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.\n\n- [[TMM](https://doi.org/10.1109/TMM.2023.3331584)] Scalable Point Cloud Attribute Compression.\n\n- [[arxiv](https://doi.org/10.48550/arXiv.2303.06519)] Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model.\n\n- [[ICASSP](https://ieeexplore.ieee.org/document/10095385)] Deep probabilistic model for lossless scalable point cloud attribute compression. [[Code](https://github.com/Weafre/MNeT/)]\n\n- [[DCC](https://ieeexplore.ieee.org/abstract/document/10125514)] Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction.\n\n- [[ICASSP](https://ieeexplore.ieee.org/abstract/document/10096559)] Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention.\n\n- [[ACM MM](https://dl.acm.org/doi/abs/10.1145/3581783.3613793)] Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block.\n\n- [[ICASSP](https://ieeexplore.ieee.org/document/10096294/)] Normalizing Flow Based Point Cloud Attribute Compression.\n\n- [[APSIPA ASC](https://ieeexplore.ieee.org/document/10317255)] Sparse Tensor-based point cloud attribute compression using Augmented Normalizing Flows.\n\n- [[ACM MM](https://dl.acm.org/doi/abs/10.1145/3581783.3612422)] PDE-based Progressive Prediction Framework for Attribute Compression of 3D Point Clouds. [[Code](https://github.com/Yanggoo/PDE-basedPointCloudCompression)]\n\n- [[TIP](https://ieeexplore.ieee.org/document/10314418)] GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute. [[Code](https://github.com/xjr998/GQE-Net)]\n\n- [[arixiv](https://arxiv.org/abs/2311.13539)] Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression.\n\n- [[TIP](https://ieeexplore.ieee.org/document/10234082)] Near-Lossless Compression of Point Cloud Attribute Using Quantization Parameter Cascading and Rate-Distortion Optimization.\n\n- [[TPAMI](https://ieeexplore.ieee.org/document/10301698)] 3-D Point Cloud Attribute Compression With -Laplacian Embedding Graph Dictionary Learning.\n\n- [[TVCG](https://ieeexplore.ieee.org/document/10328911)] GRNet: Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling. [[Code](https://github.com/3dpcc/GRNet)]\n\n- [[CVM](https://arxiv.org/abs/2209.08276)] ARNet: Compression Artifact Reduction for Point Cloud Attribute. [[Code](https://github.com/3dpcc/ARNet)]\n\n- [[TMM](https://ieeexplore.ieee.org/document/10313579)] ScalablePCAC: Scalable Point Cloud Attribute Compression.\n\n- [[ACM MM](https://dl.acm.org/doi/10.1145/3581783.3613847)] YOGA: Yet Another Geometry-based Point Cloud Compressor. [[Code](https://github.com/3dpcc/YOGAv1)]\n\n- [[unpublished](https://3dpcc.github.io/publication/YOGAv2/)] YOGAv2: A Layered Point Cloud Compressor.\n\n### 2022\n\n- [[TMM](https://ieeexplore.ieee.org/document/9447226)] Deep-PCAC: An End-to-End Deep Lossy Compression Framework for Point Cloud Attributes. [[Code](https://github.com/xhsheng-ustc/Deep-PCAC)]\n\n- [[PCS](https://arxiv.org/abs/2212.05589)] Learning Neural Volumetric Field for Point Cloud Geometry Compression.\n\n- [[IJCAI](https://arxiv.org/abs/2205.01135)] D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction. [[Code](https://github.com/ttlzfhy/D-DPCC)]\n\n- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9852261)] Isolated Points Prediction via Deep Neural Network on Point Cloud Lossless Geometry Compression.\n\n- [[ARXIV](https://arxiv.org/abs/2208.12573)] Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention.\n\n- [[ARXIV](https://arxiv.org/abs/2208.02519)] IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression. [[Code](https://github.com/I2-Multimedia-Lab/IPDAE)]\n\n- [[ICME](https://ieeexplore.ieee.org/abstract/document/9859853)] TDRNet: Transformer-Based Dual-Branch Restoration Network for Geometry Based Point Cloud Compression Artifacts.\n\n- [[ECCV](https://arxiv.org/abs/2205.00760)] Point Cloud Compression with Sibling Context and Surface Priors. [[Code](https://github.com/zlichen/PCC-S)]\n\n- [[APCCPA](https://arxiv.org/abs/2209.04401)] GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression. [[Code](https://github.com/InterDigitalInc/GRASP-Net)]\n\n- [[AAAI](https://arxiv.org/abs/2202.06028)] OctAttention: Octree-based Large-scale Context Model for Point Cloud Compression. [[Code](https://github.com/zb12138/OctAttention)]\n\n- [[CVPR](http://arxiv.org/abs/2204.12684)] Density-preserving Deep Point Cloud Compression. [[Code](https://github.com/yunhe20/D-PCC)]\n\n- [[CVPR](https://arxiv.org/abs/2203.09931)] 3DAC: Learning Attribute Compression for Point Clouds. [[Code](https://github.com/fatPeter/ThreeDAC)]\n\n- [[ICMR](https://dl.acm.org/doi/abs/10.1145/3512527.3531423)] TransPCC: Towards Deep Point Cloud Compression via Transformers. [[Code](https://github.com/jokieleung/TransPCC)]\n\n- [[APCCPA](https://dl.acm.org/doi/abs/10.1145/3552457.3555731)] Transformer and Upsampling-Based Point Cloud Compression. [[Code](https://github.com/arsx958/PCT_PCC)]\n\n### 2021\n\n- [[arxiv](https://arxiv.org/abs/2111.10633)] Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression.\n\n- [[MMSP](https://arxiv.org/abs/2106.06482)] Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds.\n\n- [[arxiv](https://arxiv.org/abs/2106.01504)] DeepCompress: Efficient Point Cloud Geometry Compression. [[Code](https://github.com/pmclSF/DeepCompress)]\n\n- [[MM Asia](https://dl.acm.org/doi/abs/10.1145/3469877.3490611)] Patch-Based Deep Autoencoder for Point Cloud Geometry Compression. [[Code](https://github.com/I2-Multimedia-Lab/PCC_Patch)]\n\n- [[TCSVT](https://ieeexplore.ieee.org/document/9321375)] Lossy Point Cloud Geometry Compression via End-to-End Learning.\n\n- [[DCC](https://ieeexplore.ieee.org/document/9418789)] Multiscale Point Cloud Geometry Compression. [[Code](https://github.com/NJUVISION/PCGCv2)] [[Presentation](https://sigport.org/documents/multiscale-point-cloud-geometry-compression)]\n   \n- [[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)]\n\n- [[CVPR](https://arxiv.org/abs/2105.02158)] VoxelContext-Net: An Octree based Framework for Point Cloud Compression. \n\n- [[ICASPP](https://ieeexplore.ieee.org/document/9414763)] Learning-Based Lossless Compression of 3D Point Cloud Geometry. [[Code](https://github.com/Weafre/VoxelDNN)]\n\n- [[RAL-ICRA](https://ieeexplore.ieee.org/document/9354895)] Deep Compression for Dense Point Cloud Maps. [[Code](https://github.com/PRBonn/deep-point-map-compression)]\n\n- [[arXiv](https://arxiv.org/abs/2104.09859)] Multiscale deep context modeling for lossless point cloud geometry compression. [[Code](https://github.com/Weafre/MSVoxelDNN)]\n\n- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9496667)] Lossless Coding of Point Cloud Geometry using a Deep Generative Model. [[Code](https://github.com/Weafre/VoxelDNN_v2)]\n  \n- [[ICIP](https://ieeexplore.ieee.org/document/9506631)] Point Cloud Geometry Compression Via Neural Graph Sampling. [[Code](https://github.com/linyaog/point_based_pcgc)]\n\n### 2020\n\n- [[arxiv](https://arxiv.org/abs/2012.08143)] NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression.\n\n- [[ICME](https://ieeexplore.ieee.org/document/9102866)] Lossy Geometry Compression Of 3d Point Cloud Data Via An Adaptive Octree-Guided Network. [[Code](https://github.com/wxz1996/pc_compress)]\n\n- [[MMSP](https://ieeexplore.ieee.org/document/9287077)] Improved Deep Point Cloud Geometry Compression. [[Code](https://github.com/mauriceqch/pcc_geo_cnn_v2)]\n\n- [[CVPR](https://ieeexplore.ieee.org/document/9157381)] OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression.\n\n- [[NIPS](https://arxiv.org/abs/2011.07590)] MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models.\n\n- [[ICIP](https://ieeexplore.ieee.org/document/9191180)] Folding-Based Compression Of Point Cloud Attributes. [[Code](https://github.com/mauriceqch/pcc_attr_folding)]\n\n- [[ICIP](https://ieeexplore.ieee.org/document/9190647)] A Syndrome-Based Autoencoder For Point Cloud Geometry Compression.\n\n### 2019\n\n- [[ICIP](https://ieeexplore.ieee.org/document/8803413)] Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression. [[Code](https://github.com/mauriceqch/pcc_geo_cnn)]\n\n- [[ICRA](https://ieeexplore.ieee.org/document/8794264)] Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks. [[Code](https://github.com/ChenxiTU/Point-cloud-compression-by-RNN)]\n\n- [[PCS](https://ieeexplore.ieee.org/document/8954537)] Point cloud coding: Adopting a deep learning-based approach. \n\n- [[arXiv](https://arxiv.org/abs/1909.12037)] Learned point cloud geometry compression.\n\n- [[arXiv](https://arxiv.org/abs/1905.03691)] Deep autoencoder-based lossy geometry compression for point clouds.  [[Code](https://github.com/YanWei123/Deep-AutoEncoder-based-Lossy-Geometry-Compression-for-Point-Clouds)]\n\n- [[CMM](https://dl.acm.org/doi/10.1145/3343031.3351061)] 3d point cloud geometry compression on deep learning.\n\n- [[TIP](https://ieeexplore.ieee.org/document/8676054)] A Volumetric Approach to Point Cloud Compression—Part I: Attribute Compression.\n\n- [[TIP](https://ieeexplore.ieee.org/document/8931233)] A Volumetric Approach to Point Cloud Compression–Part II: Geometry Compression.\n\n### 2018\n\n- [[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.\n\n### 2016\n\n- [[MM](https://ieeexplore.ieee.org/document/7405340)] Graph-based compression of dynamic 3D point cloud sequences.\n\n\n## Others\n\n- [[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.\n\n- [[MPEG V-PCC](https://github.com/MPEGGroup/mpeg-pcc-tmc2)] MPEG Video codec based point cloud compression (V-PCC) test model (tmc2).\n\n- [[MPEG G-PCC](https://github.com/MPEGGroup/mpeg-pcc-tmc13)] MPEG Geometry based point cloud compression (G-PCC) test model (tmc13).\n\n- [[CAS '18](https://ieeexplore.ieee.org/document/8571288)] Emerging MPEG Standards for Point Cloud Compression.\n\n- [[EG '06](https://dl.acm.org/doi/10.5555/2386388.2386404)] Octree-based point-cloud compression.\n\n- [[ICRA '12](https://ieeexplore.ieee.org/document/6224647)] Real-time compression of point cloud streams.\n\n### 2016\n\n- [[MM](https://ieeexplore.ieee.org/document/7482691)] Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.\n\n### 2018\n\n- [[ICIP](https://ieeexplore.ieee.org/document/8451802)] Intra-Frame Context-Based Octree Coding for Point-Cloud Geometry.\n\n### 2020\n\n- [[IROS](https://ieeexplore.ieee.org/document/9341071)] Real-Time Spatio-Temporal LiDAR Point Cloud Compression. [[Code '1](https://github.com/yaoli1992/LiDAR-Point-Cloud-Compression)] [[Code '2](https://github.com/horizon-research/Real-Time-Spatio-Temporal-LiDAR-Point-Cloud-Compression)]\n\n### 2021\n\n- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/9503405)] Lossy Point Cloud Geometry Compression via Region-wise Processing.\n\n\n## Datasets\n\n- [[KITTI](http://www.cvlibs.net/datasets/kitti/)] The KITTI Vision Benchmark Suite.\n\n- [[ShapeNet](https://shapenet.org/)] A collaborative dataset between researchers at Princeton, Stanford and TTIC.\n\n- [[ModelNet](https://modelnet.cs.princeton.edu/)] ModelNet Database.\n\n- [[JPEG Pleno](http://plenodb.jpeg.org/)] JPEG Pleno Database.\n\n- [[MVUB](http://plenodb.jpeg.org/pc/microsoft/)] Microsoft Voxelized Upper Bodies dataset.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kaiwangm%2Fawesome-learned-point-cloud-compression/projects"}