《3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder》读书笔记
3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder 题目：基于深度神经网络自编码的3D激光点云匹配定位方法 介绍： 本文将介绍一种3D点云定位方法，该方法需要一个全局的3D点云地图，以及几次连续扫描得到局部的点云子集，而无需考虑先验信息。 局部的点云子集又称为超点集。超点集是连续
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相关文章
- 1. 3D Point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder阅读笔记
- 2. 3DRegNet: A Deep Neural Network for 3D Point Registration 2020论文笔记
- 3. [CVPR 2020] 3DRegNet: A Deep Neural Network for 3D Point Registration
- 4. DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration 2019 论文笔记
- 5. [arXiv 2019] PCRNet: Point Cloud Registration Network using PointNet Encoding
- 6. 《3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration》阅读笔记
- 7. Deep Closest Point: Learning Representations for Point Cloud Registration && 2019论文笔记
- 8. Deformable MRI-Ultrasound Registration Using 3D Convolutional Neural Network
- 9. 读论文 3D Point Cloud Classification using 3D Modified Fisher Vector Representation for CNN
- 10. PCRNet: Point Cloud Registration Network using PointNet Encoding 2019 论文笔记