論文閱讀：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
論文名稱：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles 主要內容： 本文提出了一種從3D點雲中將地面點和非地面點分開的方法，並且對於斜坡型地面點有很高的識別率。 方法主要分爲兩個步驟，受限利用激光雷達的物理特徵（高度、角度）計算環間距，根據點實際間距與計算間距的比較初步去除
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相關文章
- 1. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 2. Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications閱讀
- 3. 【論文整理】Point Cloud Dataset
- 4. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 5. 【論文閱讀】2017-Segmentation-Based Classification for 3D PointClouds in the Road Environment
- 6. Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field（論文速讀）
- 7. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 8. 論文《FLAME:Feature-Likeilhood Based Mapping and Localization for Autonomous Vehicles》閱讀總結
- 9. 【論文閱讀】PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION-ISPRS-luxiaohu
- 10. 論文閱讀：PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation