kaiwangm / awesome-deep-point-cloud-compression

A list of papers about deep point cloud compression.

awesome-deep-point-cloud-compression

Papers

2024

• [ICASSP] Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction.

• [VCIP] Adaptive Entropy Coding of Graph Transform Coefficients for Point Cloud Attribute Compression.

• [arxiv] Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator.

• [arxiv] Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression. [Pytorch]

• [MMVE] Progressive Coding for Deep Learning based Point Cloud Attribute Compression.

• [TMM] Multi-Space Point Geometry Compression with Progressive Relation-Aware Transformer.

• [ICASSP] Efficient Point Cloud Attribute Compression Using Rich Parallelizable Context Model.

• [ICASSP] Efficient Point Cloud Attribute Compression Framework using Attribute-Guided Graph Fourier Transform.

• [ICASSP] ScanPCGC: Learning-Based Lossless Point Cloud Geometry Compression using Sequential Slice Representation Encoding Auxiliary Information to Restore Compressed Point Cloud Geometry.

• [IET] Point cloud geometry compression with sparse cascaded residuals and sparse attention.

• [ICASSP] NeRI: Implicit Neural Representation of LiDAR Point Cloud Using Range Image Sequence. [Pytorch]

• [TVCG] Learning to Restore Compressed Point Cloud Attribute: A Fully Data-Driven Approach and A Rules-Unrolling-Based Optimization.

2023

• [CVPR] Efficient Hierarchical Entropy Model for Learned Point Cloud Compression.

• [TMM] Scalable Point Cloud Attribute Compression.

• [arxiv] Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model.

• [ICASSP] Deep probabilistic model for lossless scalable point cloud attribute compression. [Pytorch]

• [DCC] Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction.

• [ICASSP] Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention.

• [ACM MM] Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block.

• [ICASSP] Normalizing Flow Based Point Cloud Attribute Compression.

• [APSIPA ASC] Sparse Tensor-based point cloud attribute compression using Augmented Normalizing Flows.

• [ACM MM] PDE-based Progressive Prediction Framework for Attribute Compression of 3D Point Clouds. [C++]

• [TIP] GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute. [Pytorch]

• [arixiv] Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression.

• [TIP] Near-Lossless Compression of Point Cloud Attribute Using Quantization Parameter Cascading and Rate-Distortion Optimization.

• [TPAMI] 3-D Point Cloud Attribute Compression With -Laplacian Embedding Graph Dictionary Learning.

• [TVCG] GRNet: Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling. [Pytorch]

• [CVM] ARNet: Compression Artifact Reduction for Point Cloud Attribute. [Pytorch]

• [TMM] ScalablePCAC: Scalable Point Cloud Attribute Compression.

• [ACM MM] YOGA: Yet Another Geometry-based Point Cloud Compressor. [Pytorch]

• [unpublished] YOGAv2: A Layered Point Cloud Compressor.

2022

• [TCSVT] Isolated Points Prediction via Deep Neural Network on Point Cloud Lossless Geometry Compression.

• [ARXIV] Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention.

• [ARXIV] IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression. [Pytorch]

• [ICME] TDRNet: Transformer-Based Dual-Branch Restoration Network for Geometry Based Point Cloud Compression Artifacts.

• [ECCV] Point Cloud Compression with Sibling Context and Surface Priors. [Pytorch]

• [APCCPA] GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression. [Pytorch]

• [AAAI] OctAttention: Octree-based Large-scale Context Model for Point Cloud Compression. [Pytorch]

• [CVPR] Density-preserving Deep Point Cloud Compression. [Pytorch]

• [CVPR] 3DAC: Learning Attribute Compression for Point Clouds. [Pytorch]

• [ICMR] TransPCC: Towards Deep Point Cloud Compression via Transformers. [Pytorch]

• [APCCPA] Transformer and Upsampling-Based Point Cloud Compression. [Pytorch]

2021

• [MM Asia] Patch-Based Deep Autoencoder for Point Cloud Geometry Compression. [Pytorch]

• [TCSVT] Lossy Point Cloud Geometry Compression via End-to-End Learning.

• [DCC] Multiscale Point Cloud Geometry Compression. [Pytorch] [Presentation]

• [DCC] Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression. [Presentation]

• [CVPR] VoxelContext-Net: An Octree based Framework for Point Cloud Compression.

• [ICASPP] Learning-Based Lossless Compression of 3D Point Cloud Geometry. [Tensorflow]

• [RAL-ICRA] Deep Compression for Dense Point Cloud Maps. [Pytorch]

• [arXiv] Multiscale deep context modeling for lossless point cloud geometry compression. [Pytorch]

• [TCSVT] Lossless Coding of Point Cloud Geometry using a Deep Generative Model. [Tensorflow]

• [ICIP] Point Cloud Geometry Compression Via Neural Graph Sampling.

2020

• [ICME] Lossy Geometry Compression Of 3d Point Cloud Data Via An Adaptive Octree-Guided Network. [Tensorflow]

• [MMSP] Improved Deep Point Cloud Geometry Compression. [Tensorflow]

• [CVPR] OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression.

• [NIPS] MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models.

• [ICIP] Folding-Based Compression Of Point Cloud Attributes. [Tensorflow]

• [ICIP] A Syndrome-Based Autoencoder For Point Cloud Geometry Compression.

2019

• [ICIP] Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression. [Tensorflow]

• [ICRA] Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks. [PyTorch]

• [PCS] Point cloud coding: Adopting a deep learning-based approach.

• [arXiv] Learned point cloud geometry compression.

• [arXiv] Deep autoencoder-based lossy geometry compression for point clouds. [Tensorflow]

• [CMM] 3d point cloud geometry compression on deep learning.

• [TIP] A Volumetric Approach to Point Cloud Compression—Part I: Attribute Compression.

• [TIP] A Volumetric Approach to Point Cloud Compression–Part II: Geometry Compression.

2018

• [MM] Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction.

2016

• [MM] Graph-based compression of dynamic 3D point cloud sequences.

Non-Deep Learning Methods and Library

• [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] MPEG Video codec based point cloud compression (V-PCC) test model (tmc2).

• [MPEG G-PCC] MPEG Geometry based point cloud compression (G-PCC) test model (tmc13).

• [CAS '18] Emerging MPEG Standards for Point Cloud Compression.

• [EG '06] Octree-based point-cloud compression.

• [ICRA '12] Real-time compression of point cloud streams.

2016

• [MM] Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.

2018

• [ICIP] Intra-Frame Context-Based Octree Coding for Point-Cloud Geometry.

2020

• [IROS] Real-Time Spatio-Temporal LiDAR Point Cloud Compression. [C++ '1] [C++ '2]

2021

• [TCSVT] Lossy Point Cloud Geometry Compression via Region-wise Processing.

Datasets

• [KITTI] The KITTI Vision Benchmark Suite.

• [ShapeNet] A collaborative dataset between researchers at Princeton, Stanford and TTIC.

• [ModelNet] ModelNet Database.

• [JPEG Pleno] JPEG Pleno Database.

• [MVUB] Microsoft Voxelized Upper Bodies dataset.