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<div><p>이 글은&nbsp;최근 포인트 클라우드 세그먼테이션 동향에 관한 연구를 간략히 조사한것이다.</p><p><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgcaozGMN4hw4l_3DKZNkjeXqc7Uy16p2GvN8EaXuaYEdz-mWpLTWKtvhbP2ZpmiWDnzeIE-pL-Nl4dCBux_0UMB0QTiXKisnOJ6-6fQVyQSx9yMCqIClNleCe7V95JTPRe-2zwQHbVOSmvgykSVEqcBODaeIwDl7G5o1ltswXruDAhGYkoq2ljcgv4DXg6"><img alt="" data-original-height="863" data-original-width="1848" height="130" src="https://blogger.googleusercontent.com/img/a/AVvXsEgcaozGMN4hw4l_3DKZNkjeXqc7Uy16p2GvN8EaXuaYEdz-mWpLTWKtvhbP2ZpmiWDnzeIE-pL-Nl4dCBux_0UMB0QTiXKisnOJ6-6fQVyQSx9yMCqIClNleCe7V95JTPRe-2zwQHbVOSmvgykSVEqcBODaeIwDl7G5o1ltswXruDAhGYkoq2ljcgv4DXg6=w400-h186" width="280"></a></p><br></div><p>레퍼런스</p><div><ul><li><a href="https://github.com/Yarroudh/segment-lidar">Python package for segmenting LiDAR data using Segment-Anything Model (SAM) from Meta AI.</a>&nbsp;2023</li><li>Meta AI,&nbsp;<a href="https://segment-anything.com/">Segment Anything</a>, 2023</li><li><a href="https://yarroudh.github.io/segment-lidar/">Segment-lidar documentation</a>, 2023</li><li><a href="https://ilya-fradlin.github.io/Interactive4D/">Interactive4D: Interactive 4D LiDAR Segmentation</a>, <a href="https://github.com/Ilya-Fradlin/Interactive4D">code</a>, 2024</li><li><a href="https://github.com/zeng-ziyin/PointNAT">Large Scale Point Cloud Semantic Segmentation via Neighbor Aggregation with Transformer</a>, 2024</li><li>NVIDIA,&nbsp;<a href="https://research.nvidia.com/labs/dvl/projects/sal/">Towards Learning to Segment Anything in Lidar</a>, 2024</li><li><a href="https://github.com/hrzhou2/AdaptConv-master">Adaptive Graph Convolution for Point Cloud Analysis</a>, 2021</li><li><a href="https://github.com/XiaShan1227/3D-Point-Cloud">Paper on 3D Point Cloud Processing</a>, 2024</li><li><a href="https://github.com/HuguesTHOMAS/KPConv">KPConv: Kernel Point Convolutions</a>, 2020</li><li><a href="https://github.com/QingyongHu/SensatUrban">Urban-scale point cloud dataset,</a>&nbsp;2022</li><li><a href="https://paperswithcode.com/search?q_meta=&amp;q_type=&amp;q=point+cloud">Search for point cloud | Papers With Code</a>, 2024</li><li><a href="https://github.com/unmannedlab/LiDAR-reflectivity-segmentation">Reflectivity is all you need!: Advancing LiDAR semantic segmentation</a>, 2024</li><li><a href="https://github.com/Xiangxu-0103/FRNet">FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation</a>, 2025</li><li><a href="https://github.com/UniBwTAS/continuous_clustering">Low Latency Instance Segmentation by Continuous Clustering for LiDAR Sensors</a>, 2024</li><li><a href="https://github.com/PJLab-ADG/OpenPCSeg">OpenPCSeg: Open Source Point Cloud Segmentation Toolbox and Benchmark</a></li><li><a href="https://github.com/Amsterdam-AI-Team/Urban_PointCloud_Processing">Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques</a>, 2022</li><li><a href="https://github.com/YevgeniyEngineer/LiDAR-Processing-V2">Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process</a>&nbsp;with ROS, 2022</li><li><a href="https://github.com/alexdimopoulos/PointCloudCity-Open3D-ML">PointCloudCity-Open3D-ML: Open3D-ML to integrate the Point Cloud City datasets</a>, 2020</li><li><a href="https://github.com/theobdt/aerial_pc_classification/tree/master">Segmentation of urban aerial point clouds with Deep Learning in Pytorch</a>, 2019</li><li><a href="https://github.com/theovincent/3DPointCloudClassification">Challenge to classify 3D point clouds of cities into Ground - Building - Poles - Pedestrians - Cars - Vegetation</a>, 2021</li></ul></div>

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Daddy Makers

이 글은 최근 포인트 클라우드 세그먼테이션 동향에 관한 연구를 간략히 조사한것이다.
레퍼런스
Python package for segmenting LiDAR data using Segment-Anything Model (SAM) from Meta AI. 2023
Meta AI, Segment Anything, 2023
Segment-lidar documentation, 2023
Interactive4D: Interactive 4D LiDAR Segmentation, code, 2024
Large Scale Point Cloud Semantic Segmentation via Neighbor Aggregation with Transformer, 2024
NVIDIA, Towards Learning to Segment Anything in Lidar, 2024
Adaptive Graph Convolution for Point Cloud Analysis, 2021
Paper on 3D Point Cloud Processing, 2024
KPConv: Kernel Point Convolutions, 2020
Urban-scale point cloud dataset, 2022
Search for point cloud | Papers With Code, 2024
Reflectivity is all you need!: Advancing LiDAR semantic segmentation, 2024
FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation, 2025
Low Latency Instance Segmentation by Continuous Clustering for LiDAR Sensors, 2024
OpenPCSeg: Open Source Point Cloud Segmentation Toolbox and Benchmark
Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques, 2022
Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process with ROS, 2022
PointCloudCity-Open3D-ML: Open3D-ML to integrate the Point Cloud City datasets, 2020
Segmentation of urban aerial point clouds with Deep Learning in Pytorch, 2019
Challenge to classify 3D point clouds of cities into Ground - Building - Poles - Pedestrians - Cars - Vegetation, 2021

최근 포인트 클라우드 세그먼테이션 동향

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