awesome-learned-point-cloud-compression

KO | EN

gitlite — search

#react #golang #machine-learning #deep-learning #linux #docker #macos #awesome #kubernetes #awesome-list #ios #mcp #openai #open-source

awesome-learned-point-cloud-compression

(★ 138)

A list of papers about learned point cloud compression.

File Explorer

Download Latest Version (.zip)

- - reference.md
  - SKILL.md
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

#  awesome-learned-point-cloud-compression

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
[![PR's Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat)](http://makeapullrequest.com)

## Papers

### 2026

- [[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)]

- [[arxiv](https://arxiv.org/abs/2605.01320)] PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression.

- [[ICIP](https://arxiv.org/abs/2604.28045)] TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement.

- [[arxiv](https://arxiv.org/abs/2604.04737)] LEAN-3D: Low-latency Hierarchical Point Cloud Codec for Mobile 3D Streaming.

- [[arxiv](https://arxiv.org/abs/2603.28095)] Octree-based Learned Point Cloud Geometry Compression: A Lossy Perspective.

- [[TIP](https://arxiv.org/abs/2603.25260)] Towards Practical Lossless Neural Compression for LiDAR Point Clouds. [[Code](https://github.com/pengpeng-yu/FastPCC)]

- [[arxiv](https://arxiv.org/abs/2603.23162)] LiZIP: An Auto-Regressive Compression Framework for LiDAR Point Clouds. [[Code](https://github.com/HWUDLabAIRoboticsResearch/LiZIP)]

- [[arxiv](https://arxiv.org/abs/2602.21662)] HybridINR-PCGC: Hybrid Lossless Point Cloud Geometry Compression Bridging Pretrained Model and Implicit Neural Representation.

- [[arxiv](https://arxiv.org/abs/2602.00186)] SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation.

- [[AAAI](https://arxiv.org/abs/2601.12255)] DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression. [[Code](https://github.com/zb12138/DeepRAHT)]

- [[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)]

- [[WACV](https://arxiv.org/abs/2512.22463)] MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression.

- [[CVPR](https://arxiv.org/abs/2510.20331)] AnyPcc: Compressing Any Point Cloud with a Single Universal Model. [[Code](https://github.com/Wangkkklll/AnyPcc)]

### 2025

- [[ICML](https://arxiv.org/abs/2505.09433)] SerLiC: Efficient LiDAR Reflectance Compression via Scanning Serialization. [[Code](https://github.com/3dpcc/SerLiC)]

- [[arxiv](https://arxiv.org/abs/2504.00481)] Hierarchical Attention Networks for Lossless Point Cloud Attribute Compression.

- [[arxiv](https://arxiv.org/abs/2504.14240)] ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision.

- [[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)]

- [[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)]

- [[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)]

- [[RA-L](https://ieeexplore.ieee.org/abstract/document/10876615)] H-PCC: Point Cloud Compression With Hybrid Mode Selection and Content Adaptive Down-Sampling.

- [[TII](https://ieeexplore.ieee.org/abstract/document/10884627)] suLPCC: A Novel LiDAR Point Cloud Compression Framework for Scene Understanding Tasks.

- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/10938715)] GAEM: Graph-Driven Attention-Based Entropy Model for LiDAR Point Cloud Compression.

- [[CVM](https://ieeexplore.ieee.org/abstract/document/11174068)] PCAC-GAN: A sparse-tensor-based generative adversarial network for 3D point cloud attribute compression.

- [[TIP](https://ieeexplore.ieee.org/abstract/document/10989632)] Advances in Predictive RAHT for Geometric Point Cloud Compression.

- [[TIP](https://ieeexplore.ieee.org/abstract/document/11164976)] Structure-Aware Generative Point Cloud Compression for Visual Perception.

- [[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.

- [[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)]

- [[ACM TOMM](https://dl.acm.org/doi/abs/10.1145/3715916)] Compression Approaches for LiDAR Point Clouds and Beyond: A Survey.

### 2024

- [[arxiv](https://arxiv.org/abs/2411.14501)] U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation.

- [[arxiv](https://arxiv.org/abs/2411.07899)] Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer.

- [[arxiv](https://arxiv.org/abs/2410.17823)] Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds. [[Code](https://github.com/I2-Multimedia-Lab/Att2CPC)]

- [[arxiv](https://arxiv.org/abs/2409.12724)] PVContext: Hybrid Context Model for Point Cloud Compression.

- [[ICIP](https://arxiv.org/abs/2408.10665)] End-to-end Learned Lossy Dynamic Point Cloud Attribute Compression.

- [[arxiv](https://arxiv.org/abs/2408.10543)] Diff-PCC: Diffusion-based Neural Compression for 3D Point Clouds.

- [[arxiv](https://arxiv.org/abs/2408.08682)] LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression.

- [[arxiv](https://arxiv.org/abs/2408.00599)] Learned Compression of Point Cloud Geometry and Attributes in a Single Model through Multimodal Rate-Control.

- [[arxiv](https://arxiv.org/abs/2406.05915)] Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering.

- [[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.

- [[arxiv](https://arxiv.org/abs/2405.11493)] Point Cloud Compression with Implicit Neural Representations: A Unified Framework.

- [[arxiv](https://arxiv.org/abs/2403.08236)] Point Cloud Compression via Constrained Optimal Transport. [[Code](https://github.com/cognaclee/PCC-COT)]

- [[PCS](https://arxiv.org/abs/2402.12532)] Scalable Human-Machine Point Cloud Compression.

- [[3DV](https://arxiv.org/abs/2402.07243)] PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression.

- [[AAAI](https://arxiv.org/abs/2308.12535)] SCP: Spherical-Coordinate-based Learned Point Cloud Compression. [[Code](https://github.com/luoao-kddi/SCP)]

- [[TCSVT](https://arxiv.org/abs/2305.01309)] Geometric Prior Based Deep Human Point Cloud Geometry Compression.

- [[ICASSP](https://arxiv.org/abs/2211.10916)] ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression.

- [[TIP](https://arxiv.org/abs/2207.12554)] Inter-Frame Compression for Dynamic Point Cloud Geometry Coding.

- [[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)]

- [[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)]

- [[TCSVT](https://ieeexplore.ieee.org/abstract/document/10530090)] Content-aware Rate Control for Geometry-based Point Cloud Compression.

- [[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/abstract/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. [[Code](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. [[Code](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.
  
- [[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)]

- [[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)]

### 2023

- [[MMSP](https://arxiv.org/abs/2308.05959)] Learned Point Cloud Compression for Classification.

- [[ICASSP](https://arxiv.org/abs/2303.04027)] BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression.

- [[arxiv](https://arxiv.org/abs/2301.12165)] Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding.

- [[TCSVT](https://arxiv.org/abs/2209.12512)] Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression.

- [[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. [[Code](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. [[Code](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. [[Code](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. [[Code](https://github.com/3dpcc/GRNet)]

- [[CVM](https://arxiv.org/abs/2209.08276)] ARNet: Compression Artifact Reduction for Point Cloud Attribute. [[Code](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. [[Code](https://github.com/3dpcc/YOGAv1)]

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

### 2022

- [[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)]

- [[PCS](https://arxiv.org/abs/2212.05589)] Learning Neural Volumetric Field for Point Cloud Geometry Compression.

- [[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)]

- [[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. [[Code](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. [[Code](https://github.com/zlichen/PCC-S)]

- [[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)]

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

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

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

- [[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)]

- [[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)]

### 2021

- [[arxiv](https://arxiv.org/abs/2111.10633)] Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression.

- [[MMSP](https://arxiv.org/abs/2106.06482)] Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds.

- [[arxiv](https://arxiv.org/abs/2106.01504)] DeepCompress: Efficient Point Cloud Geometry Compression. [[Code](https://github.com/pmclSF/DeepCompress)]

- [[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)]

- [[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. [[Code](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. [[Code](https://github.com/Weafre/VoxelDNN)]

- [[RAL-ICRA](https://ieeexplore.ieee.org/document/9354895)] Deep Compression for Dense Point Cloud Maps. [[Code](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. [[Code](https://github.com/Weafre/MSVoxelDNN)]

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

### 2020

- [[arxiv](https://arxiv.org/abs/2012.08143)] NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression.

- [[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)]

- [[MMSP](https://ieeexplore.ieee.org/document/9287077)] Improved Deep Point Cloud Geometry Compression. [[Code](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. [[Code](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. [[Code](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. [[Code](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.  [[Code](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.

## Others

- [[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. [[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)]

### 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.

awesome-learned-point-cloud-compression

File Explorer

Today's picks

Was this content helpful?

Recent Feedback

awesome-learned-point-cloud-compression

File Explorer

Today's picks

Was this content helpful?

Recent Feedback

Download README