Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications閱讀
該篇點雲論文主要分爲兩個階段的創新： ①點雲地面點的提取(Ground Plane Fitting, GPF)； ②點雲掃描聚類快速提取(Scan Line Run, SLR)； 一、Ground Plane Fitting, GPF 先上僞代碼圖 解釋： 第一步：首先根據輸入的點雲，進行一個按照高度的排序，選出N個高度最低的點，並求取高度平均值，作爲後續初始種子的評價，根據高度和預設的閾值進行劃
相關文章
- 1. 3D Point Clouds-1
- 2. Paper5：Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Per
- 3. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 4. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 5. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 6. 【論文閱讀】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- 7. 【論文速遞】PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
- 8. [譯文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 9. 【譯文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 10. 論文閱讀：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
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相關文章
- 1. 3D Point Clouds-1
- 2. Paper5：Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Per
- 3. Recurrent Slice Networks for 3D Segmentation of Point Clouds[CVPR2018]
- 4. Ground Segmentation based on Loopy Belief Propagation for Sparse 3D Point Clouds （論文速讀）
- 5. 【論文閱讀】【綜述】Deep Learning for 3D Point Clouds: A Survey
- 6. 【論文閱讀】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- 7. 【論文速遞】PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
- 8. [譯文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 9. 【譯文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 10. 論文閱讀：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles