Airborne-Lidar-PointCloud-Processing

This repository concludes the papers, codes and datasets about airborne lidar point cloud processing.

---

Datasets

[DALES] DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation. [seg.]

[ISPRS Vaihingen Dataset] ISPRS Test Project on Urban Classification, 3D Building Reconstruction and Semantic Labeling [seg.]

[OpenTrench3D] OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities [seg.]

[SUMS] SUM: A Benchmark Dataset of Semantic Urban Meshes [seg.]

[Toronto-3D] Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways [seg.]

[DFC2019] 2019 IEEE Data Fusion Contest data, baselines, and metrics [seg.]

[ECLAIR] ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation [seg.]

[Roof3D] Deep Learning Training Data for 3D Building Reconstruction [seg.]

[VASAD] VASAD: a Volume and Semantic dataset for Building Reconstruction from Point Clouds [seg.]

[SensatUrban ] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges [seg.]

[Swiss3DCities] Swiss3DCities: Aerial Photogrammetric 3D Pointcloud Dataset with Semantic Labels [seg.]

[DublinCity] DublinCity: Annotated LiDAR Point Cloud and its Applications [seg.]

[H3D] The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo [seg.]

[Urb3DCD] Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets. [cd.]

[STPLS3D]A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset [seg.]

Classification

2023

• Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features

2022

• Object Segmentation of Cluttered Airborne LiDAR Point Clouds

• A supervoxel-based random forest method for robust and effective airborne LiDAR point cloud classification

2021

• Airborne particle classification in lidar point clouds using deep learning

• Airborne laser scanning point cloud classification using the DGCNN deep learning method

• Airborne LiDAR point cloud classification with global-local graph attention convolution neural network

2020

• DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification

• FKAConv, Feature-Kernel Alignment for Point Cloud Convolution

2019

• Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification

• Full-waveform airborne LiDAR data classification using convolutional neural networks

Semantic Segmentation

2024

• Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering

• FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes

• Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans

2023

• Efficient 3D Semantic Segmentation with Superpoint Transformer

• Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data

2022

• Object Segmentation of Cluttered Airborne LiDAR Point Clouds

• A dual attention neural network for airborne LiDAR point cloud semantic segmentation

2021

• OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World

• SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

2020

• Method for extraction of airborne LiDAR point cloud buildings based on segmentation

2019

• ConvPoint: Generalizing discrete convolutions for unstructured point clouds

• KPConv: Flexible and Deformable Convolution for Point Clouds

• ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics

2018

• PointCNN: Convolution On X-Transformed Points

2017

• Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

With Codes

2024

• Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering[PDF][Codes]
• FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes[PDF][Codes]
• ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation[PDF][Codes]
• RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion[PDF][Codes]
• Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization[PDF][Codes]
• Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans[PDF][Codes]

2023

• Efficient 3D Semantic Segmentation with Superpoint Transformer[PDF][Codes]
• Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data[PDF][Codes]
• Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework[PDF][Codes]
• A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing[PDF][Codes]
• APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds[PDF][Codes]
• Mask3D: Mask Transformer for 3D Instance Segmentation[PDF][Codes]

2022

• City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds[PDF][Codes]
• LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD[PDF][Codes]
• Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds[PDF][Codes]
• LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers[PDF][Codes]
• SoftGroup for 3D Instance Segmentation on Point Clouds[PDF][Codes]

2021

• SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds[PDF][Codes]
• OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World[PDF][Codes]
• SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation[PDF][Codes]
• BAAF-Net: Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion[PDF][Codes]

2019

• ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics[PDF][Codes]

• KPConv: Flexible and Deformable Convolution for Point Clouds[PDF][Codes]

2018

• ConvPoint: Continuous Convolutions for Point Cloud Processing[PDF][Codes]
• Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs[PDF][Codes]
• PointCNN: Convolution On X-Transformed Points[PDF][Codes]
• 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks[PDF][Codes]

2017

• PointNet++[PDF][Codes]

Tools

• Open3D: https://github.com/intel-isl/Open3D
• PCL: https://github.com/PointCloudLibrary/pcl
• PCL-Python: https://github.com/strawlab/python-pcl
• Torch-Points3D: https://github.com/nicolas-chaulet/torch-points3d
• mmdetection3d: https://github.com/open-mmlab/mmdetection3d
• OpenPCDet: https://github.com/open-mmlab/OpenPCDet
• PyTorch3D: https://github.com/facebookresearch/pytorch3d
• Minkowski Engine: https://github.com/NVIDIA/MinkowskiEngine
• pointcloudset: https://github.com/virtual-vehicle/pointcloudset

Software

• CloudCompare: https://github.com/cloudcompare/cloudcompare
• Semantic Segmentation Editor: https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor
• MeshLab: https://github.com/cnr-isti-vclab/meshlab