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, code, 2023
- Segment-lidar documentation, 2023
- Interactive4D: Interactive 4D LiDAR Segmentation, code, 2024
- Large Scale Point Cloud Semantic Segmentation via Neighbor Aggregation with Transformer, 2024
- Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23), 2024
- segment-geospatial, Python package for segmenting geospatial data with the Segment Anything Model (SAM)
- NVIDIA, Towards Learning to Segment Anything in Lidar, 2024
- Adaptive Graph Convolution for Point Cloud Analysis, 2021
- Paper on 3D Point Cloud Processing, 2024
- OpenMMLab's next-generation platform for general 3D object detection, 2022
- Point Transformer, 2021
- 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
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