Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications阅读
该篇点云论文主要分为两个阶段的创新： ①点云地面点的提取(Ground Plane Fitting, GPF)； ②点云扫描聚类快速提取(Scan Line Run, SLR)； 一、Ground Plane Fitting, GPF 先上伪代码图 解释： 第一步：首先根据输入的点云，进行一个按照高度的排序，选出N个高度最低的点，并求取高度平均值，作为后续初始种子的评价，根据高度和预设的阈值进行划
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- 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 （论文速读）
- 5. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey
- 6. 【论文阅读】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- 7. 【论文速递】PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
- 8. [译文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 9. 【译文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 10. 论文阅读：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
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相关文章
- 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 （论文速读）
- 5. 【论文阅读】【综述】Deep Learning for 3D Point Clouds: A Survey
- 6. 【论文阅读】- 2017-A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- 7. 【论文速递】PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
- 8. [译文] PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 9. 【译文】 PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
- 10. 论文阅读：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles